Hi, and welcome to the Neil Ashton Podcast.
In each episode, we explained some of the fascinating ways
that science and engineering are changing the world around us.
We talked to leading engineers from elite level sports like
cycling and Formula One to some of the world's top academics to
understand how fluid dynamics, machine learning, supercomputing
are bringing in a new era of discovery.
We also hear some of their life stories, their career advice,
the lessons they've learned on the way that I hope will be
helpful to you too. So sit back and enjoy this
episode. Hi, and welcome back to the Neil
Ashton Podcast. So today I'm delighted to be
joined by Professor Paola Cinnella.
She's a professor of fluid mechanics at Sorbonne University
in Paris, and she's also the new director of the Sorbonne
Cluster for Artificial Intelligence, also in Paris.
And what I've found particularly interesting about her career
is that she's not come through AI, through the usual, you
know, computer science route. She is very much mechanical
engineering and fluid dynamics specialist.
We talk about where she started in Italy, the transition to
France, moving back, the idea of, you know, academia having to
move between different countries to, to secure the tenure
positions, how how challenging it, it, it can be, especially
for, you know, for family and, and personal life.
But she really, well, I liked it.
Her passion for, for fluids, you know, she talked about working
on these exotic dense gases and real gas effects and, and how
some of those LED her towards the uncertainty quantification
and Bayesian methods. And working with statisticians
helped her then realize that she could do the same around
machine learning and really wanting to collaborate with
different disciplines and, and different groups.
And, and in some ways, she's represents a very important
bridge between what we might call, you know, classical CFD,
you know, numerical methods, turbulence modelling and this
new world of, of, of AI for science.
And, and she's played actually a, a major role in the wider CFD
community. You know, she's an editorial and
chief of Computers & Fluids associate editor for
International Journal of Heat and Fluid Flow.
And she also helps to coordinate the ERCOFTAC special interest
group on Machine Learning in Fluid Dynamics, which has led to
a really successful conference series.
This ML fluids that I have been fortunate to to have helped her
a little bit as well. And, but really we, she is an
educator and she is an academic and, and we had a really
interesting discussion as well on what it means in this new era
of AI, what, what's the valuable thing that we should be
teaching new students at undergraduate and postgraduate
level? And really what is the potential
for methods? And one of the things we talk
about is, you know, the differences between surrogate
modelling and turbulence modelling and how could they be
interconnected? When is the value of those?
And and we finish off talking a little bit on the AI for science
and how there is this opportunity for cross
collaboration across discipline. So she is somebody that I am
inspired by and always enjoy working with.
And I hope you enjoy learning more about her and her work in
this conversation. So sit back and enjoy this
conversation with Paola. So yeah, thanks.
Thanks for coming on this, really appreciate it.
I've loved working with you on various projects over the years,
but this is a good opportunity to hear more about, you know,
you and, and what you do. And I guess maybe it's a
starting question. You know, you started in
mechanical engineering in, in classical fluid mechanics, I
guess, and then you move through now into, you know, machine
learning and, and AI. But how did all that happen?
You know, rewind where? Where did this love for fluid
mechanics start? Well, when I started, so I had
to choose the university. I wanted to do fundamental
physics. I, I wanted to go to, to
fundamental physics, astrophysics or something or to
mathematics. And well, my mother said, no,
this is not a good job because the only opportunity for you is
to become a researcher that's not well paid.
So be an engineer. So good for engineering.
And then I started to do mechanical engineering actually.
And after one year, I discovered
that they didn't like, and I started to look for things which
were closer to physics and to mathematics.
And well, a friend of mine said, you know, I'm doing fluid
mechanics. There's quite a lot of
mathematics in that, and also some physics.
So, right. And that's how I went to fluid
mechanics and I didn't like it.
Actually, my my professor in Italy is Michele Napolitano.
He was, he worked with NASA. He was, he had a PhD with
Stanley Rubin, who was actually the one of the first
editors in chief of Computers & Fluids.
And he gave us a book by Shapiro.
It's fast profiles. I think it's a book.
You know, Shapiro is a, is a professor.
He was professor at MIT I think.
And he said he he gave us the book the first day of the
classroom and he said if you don't like this book, you can
change, go to another take another course to just abandoned
the fluid mechanics option. And I read it and I loved it so.
And that's how I meant for the
mechanics. So that's interesting.
And then did you always want to go and do a PhD or were you
sort of debating of going into industry or something?
Yes, because you know my family, everybody.
So my family is a family of professors.
Well, they are professors more in the secondary school, but
they all did high studies, let's say university and so on.
So I wanted to be a professor and I wanted to be a researcher
most first of all, in the 1st place, I wanted to be a
researcher And, and So what I knew that was necessary to have
a PhD for being a researcher. And so well, since the beginning
I was looking for a PhD. And at that time it was very
hard to have a PhD in Italy because there was no traditional
PhD, I would say 30 years ago. And the people went abroad for
the PhD actually, because in Italy it was not very valued.
It was at the very beginning. I was maybe the 11th cycle of
PhD, which means that the, the PhD degree in Italy was created
11 years before I start, you know, so it was relatively
recent and, and there were very few fellowships for a PhD, so
it was very difficult to have them.
And that's how at some point I, I decided to move to France.
So, yeah. And and then, well, I loved
France and remained here. Yeah, OK.
And your topic was on more numerical schemes, more like
compressible to how did you get to that, you know, decision I I
guess on the. Topic Actually the very
beginning. At the very beginning I was
doing schemes for incompressible flow.
It was I started with the lid driven cavity and the velocity
vorticity formulation of the Navier–Stokes equation.
So that was my master's thesis, but then when I was looking for
a PhD for a PhD in France, actually the beginning should
have been just a second master degree.
And after I decided to remain for the PhD in France.
But anyway, so my, my supervisor in Italy knew Professor Alain
Lerat, who was one of the founders of the ICCFD
conference too. And he said, well, I know this
guy in France is doing good job with, but it's compressible.
So it's another thing. And then if you don't, you're
not scared about compressible flows, you can go to him and
he's very good. She's doing, she's doing
numerical schemes and so on. And so that's how I decided, oh,
Paris is not bad. Actually it was because of Paris
more than because of the compressible schemes, because my
options were to go to the von Kármán Institute, to go to the
US or to go to Paris. And what I said about Paris, I
have to go to Paris. And so I arrived here and
Alain Lerat proposed to me to work on high order schemes which
were a quite recent topic at that time.
You know, at that time high order was second order schemes
actually because everybody was doing 1st order.
So 2nd order scheme were already high order.
And he said to me, but we are going to move to 3rd order.
OK, yeah. And that's, you do unsteady
flows because at the, you know, at that time people were mostly
doing steady Euler or steady Navier–Stokes.
And so, well, I started a PhD for high order scheme to capture
steady phenomena, even if it was only RANS at the time.
And, and, and yes, it was a high order finite-volume schemes.
And then I spent quite a lot of my career on on high order
scheme because of that. And, and was it therefore a
natural progression to continue going down the route to sort of
post doc and, and, and you know, faculty position with was
it just an evolution or, or was it a, a challenge to, to, to
progress down that route? It was a challenge because as
you know, there are not many positions in academia.
They're very challenging. Also, when I, when I was in
France, I spent quite one year, the second master degree and
then three years of PhD in France.
And at some point I wanted to go back to Italy.
And in Italy there were zero positions basically.
So I passed the competitions in France because in France, you
know, the positions are open, you have to to get a
qualification, which is a national qualification.
So this depends on how many papers if you have taught
courses not and so on. And you it's basically sort of
minimum certification, which says, OK, you are fit to be a
professor. OK, not, not a professor, but an
assistant professor. And then we once you have got
got, once you got the, the, the qualification, you have to apply
at the universities which have positions open and you have to
compete with other guys. So I eventually got a position
here in Paris, but I wanted to go back to Italy.
So I renounced the position, even it was rank at first.
And I decided to go back to Italy.
And there I started with a post doc because there were no
permanent positions open. And after some time after my
post doc, I moved to a close by university because, well, my
home university is Bari. It's in the South of Italy, it's
Apulia. And I went to Lecce, which is
even even more south you know, it's the very tip of the heel.
And, and there I got the eventually a faculty position
and I worked there for eight years as an assistant professor.
And I was the only assistant
professor in fluid dynamics of the whole university.
I was the only one. And, and what was what was your
main focus back was back then, you know, when did this move to
the the RANS modelling and certainty quantification and
things like that? Was that during that period or
was that? Actually when I arrived in
Lecce, I was, as I said, I was the only fluid mechanician and I
wasn't part of a group, a larger group in energetics.
And I started to think about what could I do which is related
to energetics. And I discovered almost by by by
chance dense gases. And so dense gases are
compressible. So it's it's gases.
So it's compressible. Compressible flows of organic
fluids, which are governed by complex equations of state could
be super critical CO2, but it's it's light gas.
But you can have denser gases or more heavier gases like
refrigerants or hydrocarbons. And these gases are used in
processes, industrial processes, or also in some thermodynamic
cycles like, well, of course the refrigeration cycles, but also
direct cycles, which means, for instance, organic Rankine
cycles, which are like the Rankine cycle, which works with
these strange gases instead of water.
And the problem with these gases is that since it is
industrial, industrial fluids they are very ill
characterized. Actually you don't know exactly
about the properties about equational state you should use
there are few data. The material properties are
given in only technical sheets with a lot of uncertainties.
And that's how I came to uncertainty quantification,
because at some point I discovered polynomial chaos
stuff and so on. And I said, oh, this is perfect
because I have a fluid where actually don't know exactly how
it behaves. So I would like to characterize
the impact on the CFD solution of the, the, the, the bad
knowledge of the fluid properties, not only the
thermodynamics, but also the transport properties are not
very well known. And so, yeah, that's how I moved
to uncertainty quantification. But at the beginning was not
RANS, it was even Euler, you know, but you don't know exactly
the thermodynamic, you cannot characterize completely the
thermodynamic behavior of your gas because you, you, you don't
know which equation of state you should use.
And the parameters of the equation of state are, you know,
not very accurate. That's interesting.
So it's more driven from that. Yeah, I guess did that help you
working in a more complex area in a way?
Did it give you more understanding of the
fundamentals of fluid mechanics? I guess when people just study
single phase simple incompressible, maybe they don't
appreciate the. Complexity, yeah.
Actually I I adore those gases because, well at least
theoretically they could exhibit very exotic behaviors.
In particular they are expected to exhibit under some
thermodynamic conditions. If if you take a heavy enough
gas, it is expected to exhibit expansion shock waves.
And actually there is a community around that which is
called the non ideal compressible fluid dynamics
community, which has spent several years trying to expect
to to have an experimental proof of the existence of these
expansion shock waves in single phase compressible flows.
So the first one to postulate that is Hans Bethe.
So the the physician, the physicist Hans Bethe, Nobel
Prize who showed that for van der Waals gases with some values
of the of the polytropic exponent, you can get a region
where the second principle of thermodynamics forbids the the
classical shock waves. So the the the compression shock
waves. So instead you have a
compression fan and conversely, you can have expansion shock
waves and instead of expansion fan.
So everything is reversed. And then several researchers
work on that. Thompson, Michael Cramer at at
Virginia Tech and so on. And people were fascinated by
these gases because everybody wanted to prove experimentally
that that was possible. And eventually there was a group
in Russia at some point we did an experiment with FC-70, which
is a, you know, fluorocarbon, those which are very, very bad
for the ozone layer. So they did an experiment with
this and they said, oh, here, here you have the expansion
shockwaves. But then the people started to
challenge the experiment and said no, this is impossible,
Probably it was two-phase and so on.
And then in depth there was a group.
So the group of Piero Colonna, there is a big group in TU Delft
who tried to reproduce an experiment with different gases.
Also you have Alberto Guardone,
in Milan. He, he got an ERC actually on
that. And what it's very, very
difficult because this shock expansion shock waves exist in a
very tiny thermodynamic region and all the uncertainties
associated with the shock tube with the membrane breaking and
so on can perturb the actual development of the shockwave.
And so we are never sure it's really a pure expansion
shockwave. So, well, and the interest of
that thing besides the, let's say the, the, the, the
scientific curiosity was that some people were expecting that
you could exploit this behaviour to get better energy
conversion cycle with the reduced losses and so on.
It has been abandoned right now a little bit because actually
while there is a very small industry supporting that because
it's very specific machines and it's very small companies
working on that with few funds for to invest in research.
But it was a great period. So it was actually, and also it
has the impact, even if you don't find the expansion shock
waves, at some point, you don't care because there are plenty
of systems with real gas effects, not expansion
shockwave, but still real gas effects.
And that you need to characterize to have better
conversion cycles or better heat pumps or, you know, all these
stuff. And so we're still working.
So we are right now a project ongoing with the, with, with the
German team. We are doing the, the
simulations and the machine learning and they are doing the
experiments and it's a very nice team.
And yeah, we, yeah. Very cool.
And you, you mentioned at the beginning that, you know, you
were in Italy, you went to Paris, you went back to Italy.
What then was the what? What led to you, you know, going
back to Paris again? Yeah, well, again, I mean, it's
not easy to stay in academia in Italy because the, you know, the
funding system, well, the academic system in Italy is
chronically underfunded, but severely underfunded.
That's why you find Italians everywhere.
Actually, I've noticed maybe that there are lots of Italians
everywhere. So, so the, the academic
position are very few. I had one actually.
But then you are alone. It's very difficult to get
funding. The possibility of having a
career are very, So you, you have to be very patient, you
know. And also, my husband wasn't
Italian and he never could find a satisfactory job in Italy.
So at some point he said, well, let's go back to Paris.
And there I applied because I wanted to move to a
professorship. I was assistant professor, I
wanted to move a professor and I got a position in Paris.
And that's how we we moved back to Paris because he also had a
job here in Paris. Ah, OK, I understand.
So you well, as you say, Paris is not such a bad place to to to
be. Easy for two person It's easier
to find both a job in Paris than find both a job in the in
southern Italy. Yeah, but that does seem to be,
I guess, an overriding thing of academia that it's almost this
necessity to move, right. It's it's kind of a weird
profession. Even in the US or the UK, very
few people will have their entire career at one place,
particularly earlier on. They will have to have you found
that it's almost, it's a fairly unfair thing in a way
that you have to move your family and do things just to
progress. Yeah, yeah, that that that's
true. And that's because, well, for
instance, in France, there is even a rule in some, in some
disciplines, like in mathematics, that if you have
been an assistant professor in a department, you cannot be a
professor in the same department.
You have to move another one. And these small cities where you
have just one university, it's basically this means that you
have to move. So this pushes a lot of people
to abandon the idea of moving to a professorship and they
remain assistant professors for forever.
Also because the French system allows it.
It's not like the tenure track in the US.
If you don't, if you are not promoted to associate, then you
have to leave academia. You can remain as associate
professor with a permanent position forever.
So if you can't move with your family, basically you cannot
progress in your career. And and so it's a choice.
Some people are happy with the, with an assistant professorship
for their life, they do teaching and so on.
But if you, if you want to move, so if you want to progress
with the career, yes. The, the, I don't know, the
academic, the academic world is built like that.
Why don't know exactly it's also part of science to move.
We should look in the past at the the former scientists where
they were moving quite a lot. You know, even in the
Renaissance, you know, you, you, you had this, I don't know
Galileo or or Leonardo da Vinci, they were moving around.
And that's because you need to to spread knowledge to find
better environment. So I think it's nice the
difficulties when you also have a family life and you have to,
so you have two person to, to, to move.
In some cases it's easy because 1 is a flexible job or, or
things at home. But otherwise it can be, yeah,
quite challenging. And I think it's something that
the new generation don't appreciate that much because,
you know, I, I think I am what's, what's my generation, I
think generation X and we are, you know, I, I know that my
children say your generation X is the generation of suffering.
You know you were you were raised to suffer, but it's no
longer the case. But it, but it is a serious
point though, that I've, I almost feel like sometimes
academia has maybe not to be careful, but I always, I'm just
amazed that the dedication it requires to get through it and
to to become Someone Like You, you know, a top professor.
Like it feels, it's, I feel like probably many people aren't
able to get to that .1 Obviously they're not intellectual
enough. They're not, you know, capable
enough. But but also you have to have a
quiet determination. Yeah, to, to get there, which is
probably partly a good thing because it, you know, it's, it's
like a, you know, survival of the fittest, I guess.
But as you've said to some people where they just really
would love to stay where they're living because they have friends
and family and they can't, it's, it's a shame.
Well, what I used to say to my PhD students is that the
academic career is kind of similar to a career in music or
in theatre or in high level level sport.
OK, if you are a musician, if you're a pianist or if you are
an opera singer, you have to move around and it's a very
competitive field. And you do that not because you
want no quiet life, a quiet life, but because you are
passioned and you want to be on the stage and see, you know,
people around you clapping hands.
And, you know, it's a sort of, you know, I think that well,
there is a sort of narcissistic side, maybe like people
remaining in academia, but also, you know, it's it's an
intellectual work. And I guess that what if you,
you should you, you do that because because of passion,
first of all, it's not, it's not an ordinary job actually.
And you want to push the, the, the, the, the, the, the, the,
the frontiers of knowledge. You want your, your work to have
an impact and you know, you want to do something to move
things forward. And, and that's a, that's a
passion. So it's maybe you don't, you
don't make it, but you still try because you know, it's, it's
like a football player. Not not all football players are
Lionel Messi or, I don't know, David Beckham.
That's a very good. I've not heard it described that
way about a musician or sports players.
It's kind of true that they also move around a lot.
And I, I get that's the, that's the, the old thing though of
universities, isn't it, that you almost have to wait for the
person above you to, you know, retire or die to get their
position. So people end up also moving
around because there's sort of not enough where I guess in a
big corporate world, people are more able to, maybe that's
changing, But you know, in a, in like an Airbus or something,
I'm, I'm sure there's jobs for life almost just in one company
where academia. So I, I, I say that because I
think it, people should appreciate just how hard it is
to get to being a professor, that it, it's not just an
intellectual thing. It's like a determination and
passion thing, as you say, to, to, to do it.
And so all, you know, talking about, you know, wanting to be
at the forefront. I mean, you, you were quite
early on looking at learning and data-driven turbulence models
and and machine learning. You know, when did you start to
get into that? When did you get a sense that
these methods were an alternative or an enhancement on
the more traditional numerical methods sort of line of
research? Well, actually I.
Stepped into Bayesian methods when I was doing uncertainty
quantification for dense gases. And at some point I was
discussing with some colleagues of mine from from the University
of Trieste, and they had a sort of startup, I don't know if
you know this startup called ESTECO.
They produce software called modeFRONTIER.
Oh yes. Yes, yes, of course, yeah.
And so, yeah, they were actually promoting this software in
universities and, and they sold also academic licenses and so
on. And so I started to discuss with
them about my problems with uncertainty quantification in
dense gases. And they said, OK, but you could
try to solve an inverse problem and so on.
And, and, and so that I, so I started to look into the
literature and I started to be interested into these Bayesian
methods and, and I said, OK, this could work for dense gases,
but we, well, the thermodynamics is not the only source of
uncertainty because you also have the uncertainties
associated with the turbulence models.
And these are very old problems.
So there are so many turbulence models, you don't know which one
you have to choose, you don't know which parameters you should
put into them and so on. So let's try to quantify this
statistically instead of just using expert knowledge, which is
what any anybody does actually, because every, every
company actually has sort of, you know, established know-how
saying OK, for this problem, you should use the k-ω SST for
this problem, you should use the Spalart–Allmaras for this
problem, you should use that one.
And sometimes you also know that they retune the parameters.
For instance, I know that people in internal combustion engine
used to recalibrate k-ε for for having better results for,
for, for, for, Yeah, for internal combustion engines.
And but all this was was tuned by hands basically.
And I said, OK, if there are, you know, mathematical, clean
mathematical techniques to do that, let's try to use them.
And so since I was already doing direct uncertainty
quantification, I started to, you know, try to do the the
inverse uncertainty quantification.
And at some point, well, it was a bit hard for me because you
know, in mechanical engineering we are not very well trained
in, in probability and statistics.
So eventually. I, I studied that myself because
it was interesting to that actually I also thought a little
bit, but it was not enough to, you know, understand all the
details. And so at some point I was
invited by statistics department in Chile and they invited me for
two months and I was there with statisticians and
mathematicians of probabilities.
So and during the first month it was impossible to understand
each other because, you know, I was calling things with names
that they interpreted in another way and so on.
And, but at some point we started to understand each other
and that was great because some papers which were, you know,
just hieroglyphs to me, hieroglyphs started to be clear.
And, and that's where, you know, all the, the Bayesian stuff
was, was set and, and, and I could start to recalibrate the
turbulence models and and so on.
So that. So yeah, the fact of being in an
interdisciplinary environment and talk with mathematicians was
extremely useful to me, not only with engineers, even if I
love engineers, but you know, sometimes like, no, that.
That's, that's a very good point.
And I guess even to today, that's a challenge on the
machine learning side, isn't it, that maybe it's starting to
change in a course today. But I guess most people who are
doing the research, therefore who studied, you know, 5 to 10
years ago or more didn't have any of that background in
computer science or in statistics or, or sort of
methods that maybe people who did more maths, stronger maths
may, may, may do it, but engineering courses probably
wouldn't. I mean, how have you seen that
affect the machine learning side?
I know you've been quite passionate about connecting.
I know you you kicked off, for example, with the Extrality and
AirfRANS data sets. And, you know, did you try and
take some of that inspiration of your time getting familiar with
statistics and try and do the same on the machine learning
side? Yeah, yeah, definitely.
Actually, I had the chance when, when I, when I moved to Sorbonne
to be in a very interdisciplinary environment.
And at some point I, I, well, I stepped into the team.
We have a very strong machine learning team here in the
computer science department, and I discovered almost by chance
that they were doing machine learning for physics.
So I contacted them and said, OK, I'm a fluid mechanicist, I'm
trying to move to machine learning.
I know that you're doing machine learning and you're trying to
move to fluid mechanics. Can we do something together?
And so we applied together to an internal funding instrument of
the university and we got funded.
So we had this LearnFluidS team.
So it was a little bit of money to have a post doc and a few
interns and little things, but this allowed to connect each
other. And then they said, Hey, we have
this guy who is going to do machine learning methods for
CFD. So we want to, but we have no
databases. In machine learning.
We, we have MNIST. We have, you know, many famous
databases, but there are no good databases in CFD.
We want to build 1, but actually we don't know exactly how to
do the meshes because they were trying to use, you know,
OpenFOAM, and we're using, you know, snappyHexMesh.
Yeah. And of course the meshes are.
Not very good close to the wall.
And that's the point. So I said no, but you can also
use the structured meshes and so on.
And then, and then also we started to discuss a little bit
about the criteria we had to use to evaluate the results.
Not only the MSE we have discussed, but you know, and
that's how well eventually they produce this AirfRANS.
Of course, the well, the marriage is essentially the,
the, the students. It's something interesting that
this student had the background in physics the beginning, so and
he moved to machine learning too, but he had a background in
not in CFD but in physics. And so, yeah, so with this
mixture of disciplines, we came up with this, with this
database. And also they could test a lot
of baselines that the way they do it in machine learning or
they take 1 based and two baselines and they test all the
baselines. And now we are trying to do more
actually also for unsteady flows.
So hopefully we will have a new database coming out which is on
LES. So this times is not steady
RANS, it's more LES and it's actually it snapshots because
you know to to have time resolved predictions, yes.
And. And the idea is the same.
So they, they well enlighten me on machine learning
architectures because they know better.
They know, they know better. Also the, the, the sometimes
the, you know, the, the, the, the pitfalls and the you can
have in training these things because sometimes, you know, the
training is not an easy. But on the other side, I say,
OK, maybe you should look at this and that you should use
this physical criteria and so on.
And it's very, very instructive, I think, for both sides.
So I'm very happy with this collaboration.
And yeah, that's that's how we. I mean, how much have you
struggled, though to get acceptance in that the from when
you started to now? Have you how have you seen
things progress? You know, the the argument of
how well is the model just a fancy interpolation versus
actually learning the physics? You know, how much is that is a
a good thing that this is hard questions and how much of it is
almost holding things back a little bit?
Well, the. Hardest thing, Well, I started
with the turbulence for this right?
And it was hard beginning because, you know, well,
turbulence models have a lot of knowledge.
They are really incredible. They have this physics sense,
which is, I don't know, but they are also very, you know, fond of
their methodology. They, you know, the fact that
you have do you have to do the things like in a certain way?
And also the parameters has been, have been the model
parameters been tuned making a lot of compromises and so on.
So they don't like that you start playing with the
parameters, playing with the terms and so on and so on.
And for sure at the beginning the community of people who were
who was playing with machine learning or, or calibration also
calibrations of of turbulence model was not a community of
turbulence modellers was a community of people coming like
me from numerical schemes actually or from numerics in
general. And so we didn't have the right
codes for turbulence modelling, which is, you know, there is a
lot of knowledge accumulated for for decades.
And so many people were doing things that were not acceptable
actually from a strict turbulence modeling point of
view. So they were not using the right
features as the input of the turbulent model.
For instance, some people were using velocities at the
beginning, which, you know, it's it's not Galilean invariant and
so on. And and however, well, still
this model looked interesting. So everybody was intrigued with
the with the with the possibility of having it in this
model. Because in the end, if you look
at even if the to the traditional turbulence models,
they remain data-driven. They yeah, data-driven with a
human, you know, tuning the parameters.
But they are data-driven actually because you are you are
calibrating on, on some data sets which are the canonical
flows. But OK, so at some point, well
you had Chris Rumsey, which you know who, you know who decides
to organize a meeting at it was in Virginia, it was at the was
the name, the light, the lighthouse, right, the
lighthouse. So there was this mythical
meeting in 2022 where he said, OK, we are going to put around
the table classical turbulence modeler and these guys, these,
you know, these power venues with their machine learning
stuff. And and it was really
instructive because you had incredible guys there.
So it was a in honor of the 60th birthday of Philippe Spalart,
who is a great guy by the way. And you had, you know, Philippe,
you had Paul Durbin, you had all these guys, you know, you
know, plenty of things on turbulence modelling and they
were explaining things. And on the other hand, we were
trying to defend the idea of using machine learning.
So we were a little bit, you know, like how, how do you say
you attack? Attack.
But it was instructive because I think thanks to that, we
progressed a lot because we, we started with the idea, OK, it's
nice. We just fine tune a model for a
very narrow set of flows. We get better results.
We are happy with that. And now we are moving more and
more toward unifying models. And maybe, maybe, maybe I, I
maybe show ambition, but never know.
Maybe in 10 years more we could have a foundation model that's a
dream and turbulence model which could, you know, actually
realize the the the dream of turbulence model of a universal
turbulence model. I'm not sure we will get there,
but you know it is. Interesting though, because in
some ways turbulence modelling was going out of fashion.
And I know that it used to be the joke, didn't it?
I think that for a European project, if you said you're
going to work on turbulence modelling, it was almost like
guaranteed to be rejected because it was seen as a solved
problem or you know what's new? And but there were the the irony
is is the industry still uses mainly RANS and are stuck using
methods from the 80s or or early 90s seems like with
machine learning. Then there was this spike of of
of potential again. But I don't know about you, but
I almost felt that. Maybe some of the?
Use of machine learning for turbulence modeling was a little
bit early and the expectation was so big that when it didn't
meet that expectation, it sort of then dropped down again.
And, and I, I, but I agree with you that in some ways with the
idea that we want to build also foundation models from a
surrogate modelling side, having to do everything with LES or
whatever, it's just so computationally expensive.
Ironically, if you could use machine learning to come up with
the ultimate turbulence model, you would actually make it much
more affordable to, to, to actually run the simulations to
achieve. So I feel like now there is
maybe a little bit more economic or relevant again of the
turbulence modelling because it would save so much on the date
generation side. But yeah, it it does seem to be,
I don't know if you've noticed, but I saw in CFD anyway that
initially everybody was focused on machine learning for
turbulence models, where now it seems to be far more about
machine learning for surrogate models.
And that seems to be much more focused on and you don't hear as
much on maybe the turbulence modelling in such a strong way.
Would would, would you tend to
agree with that, that the community sort of shifted a
little bit? Some communities, yes, I think
in the, in the well in, in complex engineering applications
like well car industry or even in solid mechanics for instance,
they are shifting in that direction.
I think that in aerospace they are still interesting to
turbulence models, especially for complex application like
hypersonics or transition models, all that.
That's because, well, even if you can, well what you would
like to have in in for instance in aerospace where we have what
you would like to have is a high fidelity model, so like LES
quality and to perform your optimization with that.
Why? Because we know that RANS has
flaws, because we want to explore extreme, extreme
operating conditions, because we are moving away from known
paths like we are changing the fuels, we are changing the
architectures and so on. So you cannot just design, I
don't know, a new propeller or a new wing based on epsilon
modifications of something you know and for which you know that
the turbulence model is going to be wrong, but you know more or
less how it's going to fail and how you should correct it.
OK. And the problem is that even if
right now with the, the, the, the porting to GPUs and so on,
high fidelity simulation are becoming more affordable, still
that's one simulation. If you want to perform an
optimization, you need thousands of LES.
And even if you can run a complex wall-modelled LES on on
a GPU, what you know, chatting, you know, well, you know this,
but still it's 1. So if you want to perform, I
don't know, 2000 simulations, you need 2000 GPUs, which is
very costly. I don't know how many companies
can, you know, afford 2000 GPUs, But there are and also, if you
have, you know, few GPUs, well, you have to wait for many, many
days and the design cycles to be shorter for for economical
reasons and also for environmental reasons.
I mean, OK, so for that you cannot rely on LES alone.
So what you can try to do is to distill the knowledge of this
high fidelity method into lower order models.
So the how can you do it? That's the way I tried to do it.
One possibility is to distill this into augmented RANS models.
And then once you have augmented your RANS model, you use that
one to perform the optimization cycle, which is much more
affordable, provided that your model generalizes well enough,
at least on your design space. Because if the model fails as
you move a little bit far from the baseline, well, that's of
course that's useless. The other point you can do is to
surrogate models. OK, but the problem with
surrogate model is that you will never have enough LES to train
a surrogate model on it on LES.
So you are trying to do that right?
You are trying to produce high fidelity databases but they are
still relatively limited and I don't know how many of them will
be possible to produce especially for very complex very
high Reynolds number flows. And if we have so many data at
some point, what's the point of having surrogates if we can run
thousands and 10s of thousands of LES, what's the point of
having a surrogate? So I don't know.
So maybe what's what we are trying to do is to have multi
fidelity models. So where actually you train the
model using data of different origins provided that you have a
clear hierarchy and you can actually try to to train using
plenty of low fidelity data like RANS.
So that's where having a RANS, which is not too bad remains
useful. And then you try to learn the
gap between the RANS and the high fidelity and then you use
your surrogate. And this is something which is
not yet so used in the surrogate modelling community because they
either use plenty of RANS data or well, whoever some databases,
high fidelity database like the one you you have contributed
to produce. But still in a database you have
what, 300 cases for a family of cases, But it's not enough
to, you know, to have something which can be reused
for anything. So this means that every time
you change, you need to run 300 and yes, or, or one model that
yes or whatever. And that's costly.
And yeah, So no, I, I. That's kind of why I think there
is a connection between the two for sure that the the the
underlying CFD is still the key and making that more affordable.
Yeah, you're right.
There are start-ups like like Volcano and others who are
trying to come up with very computationally efficient codes.
But that is kind of the whole point of turbulence modelling in
some ways is to, is to try to come up with a way of modelling
it in a, in a, in a, in a clever way.
So, yeah, if it, if it can be done, then that would be then
that would be good. I, I did want to ask you on the,
I mean, you're an editor in chief of Computers & Fluids.
You're extremely active in the, you know, academic world when
it comes to publishing. You know, how how are things
changing with the rise of like ML based papers?
You know, what standards should they be having?
You know, you would previously we'd always say, I want to see a
mesh requirement study. I want to see proof of
convergence. I want to see, you know what,
what standards are you wanting and seeing?
And and yeah, I'd be interested to hear your thoughts as a
journal editor. Yeah, what we are.
Doing right now in Computers & Fluids is to ask to motivate
very well why you do need machine learning and why this is
improving over, I would say, standard approaches.
To give an example, many people do OK, we were talking about
surrogates and they say, OK, I want to design a new airfoil.
So new airfoil and for that I generated a database of 10,000
RANS simulations and then I trained the surrogate and then
here goes my my, my, my optimal profile optimized using the
surrogate. But if you have the
computational power to run 10,000 RANS, you can do brute
force optimization. You don't need actually the
surrogates, right. So what we are trying to ask is
to show clearly that you are learning something more than.
So your method is, is bringing something more either in term of
total computational time, including the time required to
generate the database and in terms of improved design or
showing that you, you know, you are making up for some failure
of the classical model because otherwise showing OK, I train
surrogate, it works well. Here's the optimum.
It's, you know, it's, it's not I, I believe it, it's, it's,
it's better. But you know, you are not
telling the community what's new with respect to things we, we
already are able to do. For instance, in some cases we
have tested some baselines on airfoils, you can generate an
airfoil solution of full-field RANS in 5 minutes.
You know using FreeFEM or any any code.
OK to train some surrogates you need 20 or 25 or 30 hours of of
GPU time. So in 20 hours I have largely
finished my optimization with my old RANS code.
So the point is to show that either you go to more complex
models and you can do that fastly.
So you have, you know, each RANS simulation would take, I don't
know, 100 hours and thanks to the surrogates, I'm speeding up
and I can have it in, I don't know, 20 hours or 24 hours or
it's not new. So what we are asking is that
OK, not only show that your model has been trained well and
it performs better, but also try to compare to classical
methods, show what's wrong with the classical method, what's
what what your new machine learning method is bringing.
And also show eventually where your machine learning method is
going to fail because that's also important, not only
showing, you know, positive results, but also negative
results. And that's something the
community doesn't like to do too much because it's easier to
publish when you have good results.
You know that's. That's a very good point
actually. I, I don't, I think if you look
at like NeurIPS or those papers conferences, they mandate like
a limitations section where you have to put down all the bits
where you didn't do it. And unless I'm mistaken, that
that precedent of very clearly pointing out where the
limitation is not something that's traditionally done right
in not in such a clear before the conclusions limitations.
And yeah, I've often felt that there needs to be more
transparency also from just the CFD side where things are bad,
you know, show me examples of machine learning, for example,
where the where if I pick a certain split or, or I pick a
thing, I get really bad results.
It's actually quite important. Or else the conclusion is that
these models are amazing and standard CFD's gone rather than
pointing out they're positives and and where they you failed.
The classic one I guess is in distribution and out of
distribution where like yeah. Exactly.
And the other point is to evaluate the models is as also
discussion. We, we have had as aware compare
the model on CFD criteria, not only on machine learning
criteria, because OK, mean squared errors are the standard
in machine learning. They are useful, whatever, but
they can be also, you know, misleading, because in
particular in external aerodynamics, where most of the
flow is uniform, basically you are training a neural network to
capture a constant function, and the regions where
something's going on which are close to the wall are very tiny.
So if you are wrong in that
region comparatively to the whole with all the points you
have, maybe it's not enough to have a, a clear signal on the
MSE. But you see it if you plot
velocity profiles, if you plot pressure distribution in
general, they are very bumpy. If you plot skin friction, skin
friction is terrible because in that case you are not only
reconstructing the flow field, the the the velocity field, you
are taking the derivatives. And it's well known that
approximating the derivative of a function is more difficult
than approximating the function itself, right?
And so in the machine learning community is not used to this
criteria, which are the standard criteria in CFD.
So again, I I think that if you pretend to, if you, if you say
that you are going to replace standard CFD approaches with
machine learning, then you have to show that machine learning
is able to provide the same thing that the CFD group is
able to provide and funny. Enough.
I was having this discussion on a, on a, on a paper we're
putting out where it's also the case, isn't it, that in machine
learning, because you have, let's say, 300 or 100 or 50 test
cases, you'll average over them.
And as your number where that has the same risk of some are
really bad, some are really good.
And then you have lots in the middle.
Or is it you have lots, you know, how, how bad is bad, how
good is good? And, and this isn't fully
explained just by a single number.
So this, as you say, almost doing a deeper analysis and
showing I, I guess that what someone described to me is they
to, to the early point of like hype because of the way the
results are presented today, where an R-squared value or even
L2 gives such low errors, it looks like they're perfect.
But then when they try and practice and they don't see as
good, it almost creates A disappointment, which if the
paper had shown it, they probably would not be
disappointed because it would just be meeting the expectation
where yes. So I would agree with you, part
of that is down to the way that the results have been presented.
Yeah.
There is quite a lot of overselling, but that's because
of the economical model also to continue, you know, and, and
the, and the publication system, which is a bit too, you know,
especially in, in some countries, that's not the case
in France, but in some countries, there's this huge
pressure for publication for being the 1st for, you know,
this is the store. And I think this, this, this
rush to publication publishing more and more in higher impact
in journals and so on, in the end is increasing in the, the
signal to noise ratio at, at such a point that people don't
even read the papers. They just read reviews of paper
which are automatically generated by AIs.
So there is something fundamentally wrong in this
because, you know, which should remain at a reasonable level
where humans are talking to humans because, you know, we're
trying to produce human knowledge in the end.
Because what's the point if all the AI is told to each other,
hey, what are we going to do as you?
I'm going to look at the beach.
What the beach is not really not bad, but stay on the beach all
the year. You have to find something else
to do. So, yeah, no, I think that we
should maybe slow down a little bit and do what well people used
to do some years ago, which means publish less, but publish
more thoughtful papers and wait a little bit before publishing.
And when you publish, publish
something which is more complete, you have looked to all
the consequences and you know, thought to all the possible yes.
Do you know what you mean? This is the, the good and the
bad thing of things like arXiv where there's almost a sense of
let's get it out there. We want people to see it.
It's a preprint. The preprint supposed to be
taken as well. You know, this hasn't been
reviewed yet. But unfortunately people just
take it as the main thing. And because things are moving so
fast, the, the fact that it hasn't been reviewed is
sometimes almost ignored, you know, by, by, by people in a
way. So it's, I agree with you there,
the pace necessitates sometimes to put it out, but at the same
time that feeling in your back at the head of going, well, I
could really do with another month or two to really deeply
investigate this. Is.
Balanced against the desire just to get something out, it's.
It's a tricky 1 and. On the other.
Hand the editorial The standard editorial system is being
flooded by papers. So it's becoming more and more
difficult to keep quality in the review process.
You know when when you are flooded by 10s of papers every
day, OK. And you have to process them and
you have to send them to reviewers.
You have to find reviewers who are also flooded by other
journals because there are many journals, OK, many, many
journals and more journals are, are being created and so on.
So at some point it's just, you know, just an escalation and
it's very difficult to control the quality of the papers and
what in, in fluid mechanics, things are becoming difficult,
but they are still under control to some extent because it's a
small community. But you have seen the examples
of these big conferences like NeurIPS where they have 10s of
thousands of papers, where most of the papers are written by AI
and reviewed by AI. And so what's, what's the
meaning of the review process in that case?
You don't review it at all. You just put it for free on the
website and who wants to read it?
So I think that the, the, the rush to having more and more and
more papers, which is also due to the, you know, to the
publishers is in some sense killing system.
Because if the review level falls below a given pressure, at
some point, there is no point in going through a standard
review process because there is no added values in going
through that review. So you just produce your your
paper, you put it somewhere and the people look at it if they
want and that's it. And if you are in the hype, you
are in, let's say, a famous team, very visible and so on,
people will have followers who will read your paper because
it's you. So we are shifting from
scientists to influencers. And that's a bit, you know,
scary for me. Yeah.
Yeah, no, no the. So how does this, you know,
translate and what it what's what are your trying to achieve
with with, you know, the new position at Sorbonne AI centre?
Could you maybe tell a little
bit more about your place and and kind of your vision to how
to do, I guess, AI for science and AI for engineering and and
and maybe take some of those rigorous things that you've
taken from the fluid mechanics world in into this side?
That'd be. I love your shiny new office So
I wanted to hear more. Yeah.
Well, actually, well, in Sorbonne we have this centre,
SCAI Sorbonne. The beginning, the name was
Sorbonne Centre for Artificial Intelligence, which was created
seven years ago at the beginning of the AI hype, because we had
the strong teams in computer science and also in applied
mathematics working on that. So basically at the beginning,
SCAI has been created by applied mathematicians and and
computer scientists alongside with some people from
humanities and from other sciences, for instance of
computational biology, this kind of thing.
And well, it has helped a lot in in making.
So in making AI, it's a one visible.
They have generated a lot of activities we have.
So at at some point we have been labeled by the French government
as clusters. So now the C in SCAI stands for
cluster. And these clusters are sort of
centres of excellence in France. There are nine of them which are
supposed to foster research in AI training and transfer to to
companies. And well, every cluster has its
own specificities. But when?
Well, based on my own experience and based on the very large
scientific community at Sorbonne, I said, well, right
now everybody is looking at AI for science because science is
actually going to be deeply modified.
The way of doing science is going to be deeply modified by
the introduction of these AI approaches, which is not the
machine learning approaches for, you know, improving predictions
or speeding up predictions, but also reasoning tools and, you
know, models for, you know, for, for automating labs,
automating labs, AI agents and, and all that.
And I said, well, the, the difficulty with that is that
when you apply this to science, you have several problems.
First of all, you are not dealing with random data.
It's not images, it's not video. It's something which has a deep
physical meaning, which he's known only to experts from the
discipline. So you can train a surrogate or
whatever, or a foundation model using, I don't know, chemistry
data. But if you don't have a
chemist by you to explain, if what's coming out of the model
is you know, makes sense from a chemical point of view or is
just an hallucination, you don't know.
So that's where it's extremely important to connect people from
computer science and the disciplines and also people from
mathematics because they can help with the mathematical
foundations. What I dream about of is the
equivalence, you know, Lax equivalence theorem for machine
learning. That would be the grail, you
know, something that ensures that you, if you put more data
in your model, at some point you are going to converge to
something. But you know, that would be
great. But so the idea would be to have
all these people discussing together.
And then I realized that some problems you can have in one
discipline, for instance, in high energy physics, you have
big data. This data are structured, they
encode causalities because the particle I'm capturing here at
this moment comes from a jet which has originated there and
the particle have undergone some decay process and so on.
And this, they call this actually jets of particles.
And they say, oh, this is the same you can have in a turbulent
jet because the jet is originating from, you know, as
lice or something as law. And then you have causalities of
turbulent, of laminar structures which eventually
become turbulent and then they are modified, they are
transported. So we have the same problems and
we have to encode these causalities because trying to
learn all the physics, all the chemistry, all the biology from
scratch, using brute force training on data, it's not
efficient because we know the physics.
If we know the physics, let's use it.
But the problem is then how do you really encode that physics?
First of all, you need to know it.
So it's not the computer science guy who is going to know which
are the constraints which are the most suitable for high
energy jets, right? So we need the communities to
work together and also some problems which have been solved
about causalities. I don't know in high energy in
the energy physics community can be useful for fluid mechanics
and cover city. For instance, I have talked with
people from plasma physics. So in some cases they have
Navier–Stokes plus the magnetic fields.
They have problems of closures because they cannot resolve all
the scales they need to coarse- grained models.
So they have closure problems, and maybe something of what we
are doing with turbulence modeling can be reused in that
field. I'm collaborating with
volcanologists because in volcanoes you need to
characterize the rheology of magmas and you cannot measure it
and you have simplified models which are not accurate enough.
So you can use the techniques we use for turbulence modelling
to improve the rheology of magmas.
So that's where it becomes exciting to share knowledge
across the, the different scientific communities instead
of having every guy in its isolated department reinvent
everything from scratch because we are reinventing the wheel
otherwise, you know, and that's how we are trying to, you know,
I'm trying to, to, to, to animate this community.
And we have a seminar now called the AI for Science.
And hopefully we are trying to apply to funding opportunities,
even if they are not funding, just the fact that we are around
the table and we are thinking about what we could do together.
It's already something which
helps building the community. And then what people start to
collaborate and I think it's beneficial to everybody.
That's, that's my hope at least.
No, it sounds. Fantastic.
And I totally agree there's lots of I like the ideas that these
multidisciplinary centres, particularly in the age of AI.
I think it makes complete sense.
And as you say as well helping be a bridge to I guess the
industry and startups and and and things like that.
That makes makes complete sense.
I guess maybe to, as we come to the end, all of these new
things, all of these topics that have come up since, since I
guess you did your original studies, you know, how does this
affect the education system? You know, what should young
fluid mechanics researchers be be picking today as their PhD
topics or the undergraduate what what will prepare them for the
next 10 to 20 years of of the world we live in?
Yeah, I. Think we are in a very critical
period for education because well you and me have been
educated in a few without AI. So we have been taught how to to
solve problems without the help of AI to how to like to
interpret critically the results and how to look for information
and, you know, how to, to check if what we are doing is good or
not and so on. The problem is that the new
generation is AI native. So I, I was hearing, yeah, yeah.
Because yesterday at the radio
were saying that many teenagers now who are going to vote for
the first time are asking AIs what should they vote?
Because trust AI is more than politicians or more than the
classical media. OK.
And they're doing a little bit the same with the with the
school, with school teachers and with university professor who
are not prepared to that because, you know, the students
have smarter than we are and they know how to, you know, to
use AI to solve their fluid mechanics problems, to solve
their thermodynamics problems. And you have still teachers who
six months ago, we're not even imagining that the problem had
been solved by an AI. So right now, many, many
colleagues are scared about the fact that students are cheating
with AI. But the for me, the problem is
not only cheating. The fact is that some students
are basically, you know, delegating the task of thinking
to AI. That's more much more dangerous.
So what I think is that we have to rethink the whole system and
many tasks which were, you know, I teach you how to solve a
second order linear ordinary differential equations and you
just have the technique to solve that.
That's no longer useful because you know, ChatGPT knows to do
that very well. The problem, we should teach the
students how to analyze the results and how to understand if
the result is correct, if it's an hallucination, how to
formulate the questions also. Because if you don't know how to
ask AI correctly, what to do, AI is going to do something you
know you don't control. And if you are not cultivated
enough to understand the answer, you are just going to accept
whatever comes out without, without, you know, without
criticizing what what's coming out.
So I think that we have to move the shift.
So instead of teaching techniques and, you know,
techniques or, or just notions, we should teach critical
thinking. And that's harder because it,
it, it, it implies a, a considerable modification of our
teaching programs and new teaching approaches.
It's not easy to do, especially in large universities as we
have, I, we have courses with, you know, 500 students.
It's not easy to, you know, to have to, to change all the
teaching procedure. But it's something we need to do
because without critical thinking, basically humans are
going to lose competence. And yeah, and, and, and, and,
and in case, in case AI is not working, what do we do?
And I had a very nice metaphor from a person from the European
committee who said, well, when you, when you are, when are you
are a pilot of an aircraft, you are 9, I say 8000 or 99900.
Ninety, 100% of the time you are going to use the automatic
pilot. But in case of accident, the
pilot needs to know how to drive the plane down to safety, right?
And that's exactly that.
So we, we need to be critical because we don't know where the
AI is going to hallucinate even we are.
Also, we need to, to understand if, for instance, sometimes AI
is able to bring us notions from other disciplines which are
not our discipline. But if we have methodology, we
know how to analyze if what the, the, the, the LLM is saying is
reasonable or not. We know how to verify facts and
so on. And we have culture, general
culture and language capacity to understand then we can control
and we can be enriched as professionals, as scientists, as
humans and whatever instead of being just, you know, replaced
like in Matrix. That's my, my nightmare.
You know, Matrix humans are just there to be pumped energy.
Yeah. No.
It does. It does seem that it's not
universally accepted. How best to do this from an
education point of view? I've heard some go the other way
and be almost well for an undergraduate, we will do lots
of in person examinations and basically no homework anymore
because you this is no way of knowing the homework.
And, and as you say, there's the other side, which is maybe a
little bit more ex, you know, do a presentation to explain how
you got there. I, I kind of lean a little bit
more to the first one at the right stage of your career.
I guess this is more like even a secondary school, let's say,
you know, before university, you know, the certain things where
I just feel you have to know it because as you say, what if
there is a moment where you don't, you're on a, you know,
you don't have access to the AI tools.
I feel there's like a transition point when you can expect.
Like if you're doing a P. HD.
Or certainly a. Postdoc.
I actually think AI is a fantastic help and will make you
more productive, make you be able to do more things, help you
achieve what you want to do faster and better, and
ultimately get a, you know what would have taken you a month to
write some coding, which wasn't your main task, it was just you
had to do it to get there. AI will accelerate it.
But if you're at the undergraduate level or or still
at school or something, I feel that's probably where AI should
only be used to help you learn something, but not it shouldn't
be used like a calculator almost or or or you will.
Just not. Understand the fundamental
theory, it can be used. As a mate you know because you
can no say tell the AI ask me questions and give the answer
and then the AI can analyze the tutor.
Yes, and it. Makes, you know, learning more
interactive. In my research, sometimes I feel
alone and I don't have a colleague to discuss about the
point. So I take an AI and say, what do
you think about this idea? And so we start the conversation
and that's useful. And then, you know, the AI is
bringing some ideas. So, yeah, yeah.
Or. Explaining new topic.
I often do that when I'm trying to and you can ask it.
And the great thing I find is that it has these different
levels where you could say, OK, sometimes I'll ask like, can
you show me that in a code like how would you actually code
this up? Which would be hard for a tutor
to do instantly like a person to be that flexible to move from
reasoning. To code and back and so on.
Because you are, yeah, easily lost in, you know, coding
details and so on That that yeah, that's that's actually
great. But the problem is to explain to
the young generations that that's the way probably to use
the AI more like a companion then like an entity that is
going to do the work in my stead while I'm, I don't know, playing
video games or something. That's that's not the point.
And, and they need so the problem is to explain them what
are the risks and why they are missing something if they use AI
only for that. So I can understand that
sometimes you need to accelerate, you know, you have
to give a report or you are a little bit in a hurry.
But if you do that systematically, then basically
you are missing your education because it's not you who is
going to be educated, but the AI and and I and I think.
In some ways it'll end up creating a bit of a two-tier
system where in some ways people will become it's quite capable
of doing some jobs where it requires them just to be, you
know, a functional user of these tools.
But it will stop you from maybe being the the inventor or the
innovator. And there it there feels like
you could, to be fair, you could probably still do quite good at
a job if you because these AI tools are so good.
But I feel there must be some points where that lack of deep
understanding will come and bite you.
And so, yeah, it's it's a challenge.
Yeah. We're not in very.
Specialistic fields as as as so we are in very specialistic
fields. So I don't think that the AI has
been trained enough. So that's where you know, you
bring the new ideas, maybe the AI strengthens you, accelerates
you with the coding or allows you to make connections more
quickly and so on. But it's still new.
We're bringing the ideas. And if you're you know although
that. I do suspect will also be, you
know, succeeded by AI because the, you know, the, the AI
scientist or the AI engineer. I have to say, I, I, I still
suspect actually that it will be able to come up with as many
ideas as as we, we will be able to do.
Maybe not the, you know, Einstein new completely
breakthrough thing, but it's it's certainly going to be an
interesting test. Of given.
That the whole, like your centre is a good example of it.
I, I do get the sense that AI for science or AI for
engineering is now becoming the frontier.
It wasn't five years ago. It was sort of seen as a niche
area. I, I get the sense now that the,
the world of sort of physical AI and AI for engineering and AI
for science is becoming a hot topic.
And whenever it's a hot topic and there's lots of investment
and money put into it, it, you know, yeah.
There is, yeah, some overselling, yeah.
But but yes, I guess, I guess that's the frontier.
And I, well, I want to believe that.
Well, even if the guys are becoming more and more powerful
and potentially they could even win a Nobel Prize at some point
if you think about AlphaFold for instance.
It's well. What's new is the fact that in a
few years they could discover thousands and hundreds of
thousands of proteins which are much more than the 2000 and
something proteins they had discovered during the past 50
years. So every protein structure was
PhD thesis and now you can generate plenty of them, you
know, just with with with these AI.
So what was new was the fact of being able to accelerate so
much. But still without the knowledge
of the proteins from the past, it wouldn't have been possible
to have that. And the same for climate.
If now climate models are so good, that's because for 10s of
years people have developed better and better climate model,
the classical ones, and they have done data simulation and
then they have done all the reanalysis from the beginning.
And with that they have generated the huge databases
which can be used for by the models.
And also, well, everything is like that.
That's because we have huge amounts of human knowledge
accumulated over years, which can be injected in these models.
But what once all the knowledge
will be generated by by these things?
Probably, I don't know, Maybe at some point they will become so
intelligent with these emergent phenomena, they will be able to
create something new that happens in very complex systems.
But I, I, I I I'm. But I, I hope humans has still a
role to play. Oh yes.
And that. Well, and that instead of being
replaced by these AIs, well, one of the person who
participates, a Jesse Thaler, he's a, he's a physicist from
MIT and he participated to our AI for science launch day.
And he came out with his metaphor of the Centaur
scientists. And I found that as a beautiful
metaphor because as you know, if you are two men and not enough
horse, you are too slow. If you are two horse and not
enough man, you cannot reason. But if you have 1/2 man half
horse, you can go as fast as a horse while having all, you
know, the creativity and and width of a human.
And so, well, I, I adopted this idea and say that, well, our
role would be to generate to, to, to train the next generation
of Centaur scientists and Centaur professionals and not
just horse. I like that.
You know, and that's, that's probably a great way to end this
in the, in the sense of I agree with you that it's, it's about
the coupling of, of human and AI.
It's about being progressive and optimistic about AI, but not
forgetting the, the sort of human role role in it.
And, and I would argue that today the human specialist is
needed even more because, you know, to, to help develop these
AI models and make them accurate and point them in the right
direction. But maybe we need to talk again
in two years time and, and see where things have gone.
It'd be interesting to to listen back and see as it shot off
like this is, is it the same? Is it less?
That will be an interesting, but I really enjoyed this
discussion with you and I personally always love working
with you on on various committees and things.
And yeah, been a pleasure to have to spend this time with
you. OK.
Thank you for the interview. It was very, very nice to
discuss about all this. Great.
Thank you.