
From models of galaxies to atoms, simple AI shortcuts speed up simulations - DarkContinent
https://www.sciencemag.org/news/2020/02/models-galaxies-atoms-simple-ai-shortcuts-speed-simulations-billions-times
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dukoid
For some reason this reminds me of the famous xerox copier where the
compression algorithm would swap out digits:
[https://news.ycombinator.com/item?id=6156238](https://news.ycombinator.com/item?id=6156238)

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choeger
I doubt it.

Simulations based on PDEs, ODEs, or DAEs have a particular mathematical
foundation. Within the bounds of their solvers, they deliver precise results
and can actually _forecast_ physical behavior.

If such an "emulator" is much better in many cases but completely wrong in
just one, it is basically useless, as presumably the verification of a
solution takes as long as a classical simulation.

~~~
siver_john
The problem with that assumption is that while those solvers are based on a
mathematical foundation (though I'd argue that some NN based simulations are
too some papers out of Weinan E's lab at Princeton having some decent math to
an untrained eye), the parameters that are fed into those are often just fit
to some experimental data. And are often only verified in a heuristic manner.
So while a neural net based simulator may not be transferable in the same way
as a more general simulation engine (though depending on how you write them
even they generally have limits), it may be fine in a certain domain which you
can show by just checking how well it fits to certain experimental data you
already have in existence.

~~~
whatshisface
This creates a very serious issue for epistemic leakage. If the sim is
verified based on human eye heuristics, then it will be possible and very
tempting to accidentally make the neural net satisfy those heuristics
specifically. Then, scientists may use the heuristics to validate the results
of a neural net designed specifically to pass their eye test, thinking that
they are kicking the tires while in reality they are learning nothing.

~~~
siver_john
Disclaimer, I am more referencing classical molecular dynamics in the atomic
region. And what I am envisioning this type of thing for is not dependent on
the mathematical model per se. So specifically my point is towards say GROMACS
(or a similar MD simulation engine) where the force fields generally used for
that are parameterized for biological/organic systems. Let's say we train our
neural network on data fitting to lipids. So maybe a bunch of random data on
lipids like their melting temperature, surface temperature, etc. Then we run
the neural network simulator to model how these things form into larger
structures (something that is for the most part impossible with current all
atom approaches). And then we study that data to make an assessment on how
certain structures can form etc.

Now if you are aware of the field, what I just described was (ignoring I do
not remember the exact fitting data) the parameterization process of the
MARTINI force field which is sufficiently good at lipids, kind of okay at
proteins, and pretty bad at everything else. But within the bounds that you
know the weakness of the system you can still use it to figure out
experimental data. (Also as an aside MARTINI only got access to proteins and
other things later on, thankfully force fields improve over time.)

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Fomite
Interestingly, my lab has been working in emulators for one of our simulation
models, and we're _really_ struggling to make meaningful improvements.

It's faster, but we're not there yet on accuracy.

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fxtentacle
"When they were turbocharged with specialized graphical processing chips, they
were between about 100,000 and 2 billion times faster than their simulations."

Now the critical question is: How much faster is it without AI, just because
of the specialized dedicated processing chips?

Otherwise, they might be comparing a single virtualized CPU core against a
high-end GPU for things like matrix multiplication ... and then the result
that GPU > slow CPU isn't really that impressive.

~~~
allovernow
>Now the critical question is: How much faster is it without AI, just because
of the specialized dedicated processing chips?

Based on similar work we are doing at the startup I work for, this isn't just
GPU magic. ML is a heuristic alternative to simulations which already operate
on specialized GPUs and TPUs. This modeling acceleration is one of the many
ways in which ML is poised to change everything.

The same way that a human can, for instance, approximately draw iso-
temperature lines around a candle flame, without having to perform
simulations...except the neural net is some 99%+ as accurate and detailed as a
full simulation. That's exactly why neural nets excel - they learn complex
heuristics much like humans do, but with the added power of digitized
computation and memory.

~~~
tomp
Don't most really complex physical calculations / simulations (e.g. weather,
planetary movements etc.) involve chaotic interactions? So an NN being 99%
correct will still result in "catastrophic" differences down the line?

~~~
whatshisface
So will quantization error when you grid your conventional sim. If you can
make your cells and timesteps smaller with the NN than without, the loss in
accuracy in one place could be compensated by the gain elsewhere. The only
issue is, good luck with developing a formal theory of error propagation
through your trained network that is faster to compute than the conventional
sim itself. Sometimes you care about strict formal guarantees about sim error
and other times you don't.

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willis936
I was at a talk last week where the speaker spent a little bit of time on
using machine learning on a regression matrix that is trained by the results
of a simulation. The simulation and variables in the regression matrix were
chosen such that the AI could recreate an approximation of a known physical
law. This is fairly exciting to me because if used to recreate a lot of laws
in this field, it could then be used on experimental data to untangle some of
the mess and identify the relationships for us. I could see this speeding
along development of science.

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aimoderate
> It randomly inserts layers of computation between the networks’ input and
> output, and tests and trains the resulting wiring with the limited data. If
> an added layer enhances performance, it’s more likely to be included in
> future variations.

Sounds a lot like genetic algorithms but with neural networks. I suspect we'll
see more of this as people figure out how to run the search over neural
network architectures that fit their own domains. Convolutions and
transformers are great and all but we might as well let the computers do the
search and optimization as well instead of waiting on human insights for
stacking functions.

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joe_the_user
The underlying paper was previously discussed on hn here:

[https://news.ycombinator.com/item?id=22132867](https://news.ycombinator.com/item?id=22132867)

Note: The published paper is titled "Up to 2B times acceleration of scientific
simulations with deep neural search", which can raise some hackles, including
mine. Doesn't _prove_ anything but still.

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RoboTeddy
Here's a potential way to use adversarial techniques to generate training
examples that could improve the accuracy of this approach:
[https://twitter.com/RoboTeddy/status/1228828411050655744](https://twitter.com/RoboTeddy/status/1228828411050655744)

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chewxy
Who'd think compression works so well?

(yes, neural networks are compression engines)

~~~
tanilama
Compression in your context is as meaningless as Generalization.

Yes, you can say generalization is compression.

~~~
fxtentacle
Except that "generalization" implies that it works for previously unseen
problems, which is usually not the case for AI.

Compression, on the other hand, nicely captures the "learn and reproduce"
approach that using AI entails.

~~~
tanilama
Unseen problems is a ill defined term. There is a distinction between in
domain and out of domain, both can be unseen by the model before.

Even human as agent requires training before being deployed to unseen
problems. Generalization is conditioned on experience, after all.

AI generalizes to unseen in domain data given a specific task. That is why it
is useful in the first place.

