Google has also a global weather model yielding by ten day predictions, and open street map runs local as well. Just today with GraphHopper and a map of Europe I can generate 2700 routes per second on my workstation. When I was young these were not things you could run at home!
This is the important part. It's not guaranteed to be accurate. They claim it "delivers essentially the same correctness as the model it imitates -- sometimes even finer detail". But can you really trust that? Especially if each frame of the simulation derives from the previous one, then errors could compound.
It seems like a fantastic tool for quickly exploring hypotheses. But seems like once you find the result you want to publish, you'll still need the supercomputer to verify?
I have a thing where I immediately doubt any ML paper that imitates a process then claims that the model is sometimes “even better” than the original process. This almost always means that there is an overzealous experimenter or a PI who didn’t know what they were dealing with.
I'm not sure what you mean by that. Neutral networks are pretty good statistical learning tools, and in this kind of application you'll need some stochastic learning, regardless of using a laptop or a supercomputer. It's not like they used an LLM to predict the simulation steps. If you read the paper, they seem to use a simple fully-connected 5-layer neural network architecture, which is a completely different beast from, say, trillion parameters transformers used for LLMs.
I don't know if it's the same thing, but it feels like an analogy:
Protein structure prediction is now considered to be "solved," but the way it was solved was not through physics applied to what is clearly a physics problem. Instead it was solved with lots of data, with protein language modeling, and with deep nets applied to contact maps (which are an old tool in the space), and some refinement at the end.
The end result is correct not because physics simulations are capable of doing the same thing and we could check Alphafold against it, but because we have thousands of solved crystal structures from decades of grueling lab work and electron density map reconstruction from thousands of people.
We still need that crystal structure to be sure of anything, but we can get really good first guesses with AlphaFold and the models that followed, and it has opened new avenues of research because a very very expensive certainty now has very very cheap mostly-right guesses.
When it comes to very complicated things, physics tends to fall down and we need to try non-physics modeling, and/or come up with non-physics abstraction.
It's an approximator, right? I don't know about Astronomy but there are obvious use cases where an approximate result is "good enough" and even better than a precise result if it's significantly cheaper (or faster!) to get the approximation than to calculate the precise result.
In cases like this I'm always thinking of Reimann integrals and how I remember feeling my face scrunching up in distaste when they were first explained to us in class. It took a while for me to feel comfortable with the whole idea. I'm a very uh discrete kind of person.
As an aside, I consider the kind of work described in the article where a classic, symbolic system is essentially "compiled" into a neural net as one of the good forms of neuro-symbolic AI. Because it works and like I say there are important use cases where it beats just using the symbolic system.
Neuro-symbolic AI can often feel a bit like English cuisine where stuff is like bangers (i.e. sausages) and mash or a full English (breakfast), or a Sunday roast, where a bunch of disparate ingredients are prepared essentially independently and separately and then plonked on a plate all together. Most other cuisines don't work that way: you cook all the ingredients together and you get something bigger than the sum of the parts, a gestalt, if you like. Think e.g. of Greek gemista (tomatoes, bell peppers and occasionally zucchini and aubergines stuffed with rice) or French cassoulet (a bean stew with three different kinds of meat and a crusty top).
Lots of the neuro-symbolic stuff I've seen do it the English breakfast way: there's a neural net feeding its output to a symbolic system, rarely the other way around. But what the authors have done here, which others have also done, is to train a neural net on the output of a symbolic system, thereby basically "cooking" them together and getting the best of both worlds. Not yet a gestalt, as such, but close. Kind of like souvlaki with pitta (what the French call a "sandwich Grecque").
Yet the idea of using the emulator to narrow down the viable space and then verifying with high-fidelity runs is still a huge win in terms of efficiency
Nice work, Marco! I'm glad to see emulators being built for DESI. I worked on emulators for DES.
Emulators have existed in astrophysics long before ML became part of the zeitgeist. The earliest cosmology emulator paper that I'm aware of is from 2009 here: https://arxiv.org/abs/0902.0429. IIRC the method came from fluid dynamics. It just so happens that the interpolators used under the hood (clasically it was GPs but is NNs latelys) are also popular in ML, and so the method gets lumped into the ML pile.
The key difference between emulation and typical ML is that emulation is always in an interpolation setting, whereas typically predictive ML is extrapolating in one way or another (e.g. predicting future events).
I’m glad this kind of work is getting highlighted on HN, but this is an extremely misleading title, to the point of being outright false.
As often happens, this appears to be due to PR titles being controlled by non-specialists, not the study authors.
While the work the authors do is important, in no sense does the tool they produced actually run a simulation.
A simulation implies a physical model and usually partial differential equations that are often solved on supercomputers, but here the neural network is rather interpolating some fixed simulation output in a purely data-driven way.
The simulations have not gotten faster due to neural networks, cosmologists have just gotten better at using them. Which is great!
Edit: see the sub-comment in the thread by crazygringo for the lead author’s take
Not cosmological but yesterday Apple released an interesting protein folding model with 3B param transformer-based arch which runs on M-series hardware and is competitive with state-of-the art models. [1] Code [2]
words fail, simpulations, educated speculations, or whatever, the main thing is to preface these sorts of thing with some indication of there limitations and purely theoretical results, use them as such, and normalise the process of asking if the model has been verified with an observation
19 comments
[ 3.6 ms ] story [ 51.4 ms ] threadThis is the important part. It's not guaranteed to be accurate. They claim it "delivers essentially the same correctness as the model it imitates -- sometimes even finer detail". But can you really trust that? Especially if each frame of the simulation derives from the previous one, then errors could compound.
It seems like a fantastic tool for quickly exploring hypotheses. But seems like once you find the result you want to publish, you'll still need the supercomputer to verify?
Physicists have been doing this sort of thing for a long time. Arguably they invented computers to do this sort of thing.
Protein structure prediction is now considered to be "solved," but the way it was solved was not through physics applied to what is clearly a physics problem. Instead it was solved with lots of data, with protein language modeling, and with deep nets applied to contact maps (which are an old tool in the space), and some refinement at the end.
The end result is correct not because physics simulations are capable of doing the same thing and we could check Alphafold against it, but because we have thousands of solved crystal structures from decades of grueling lab work and electron density map reconstruction from thousands of people.
We still need that crystal structure to be sure of anything, but we can get really good first guesses with AlphaFold and the models that followed, and it has opened new avenues of research because a very very expensive certainty now has very very cheap mostly-right guesses.
When it comes to very complicated things, physics tends to fall down and we need to try non-physics modeling, and/or come up with non-physics abstraction.
In cases like this I'm always thinking of Reimann integrals and how I remember feeling my face scrunching up in distaste when they were first explained to us in class. It took a while for me to feel comfortable with the whole idea. I'm a very uh discrete kind of person.
As an aside, I consider the kind of work described in the article where a classic, symbolic system is essentially "compiled" into a neural net as one of the good forms of neuro-symbolic AI. Because it works and like I say there are important use cases where it beats just using the symbolic system.
Neuro-symbolic AI can often feel a bit like English cuisine where stuff is like bangers (i.e. sausages) and mash or a full English (breakfast), or a Sunday roast, where a bunch of disparate ingredients are prepared essentially independently and separately and then plonked on a plate all together. Most other cuisines don't work that way: you cook all the ingredients together and you get something bigger than the sum of the parts, a gestalt, if you like. Think e.g. of Greek gemista (tomatoes, bell peppers and occasionally zucchini and aubergines stuffed with rice) or French cassoulet (a bean stew with three different kinds of meat and a crusty top).
Lots of the neuro-symbolic stuff I've seen do it the English breakfast way: there's a neural net feeding its output to a symbolic system, rarely the other way around. But what the authors have done here, which others have also done, is to train a neural net on the output of a symbolic system, thereby basically "cooking" them together and getting the best of both worlds. Not yet a gestalt, as such, but close. Kind of like souvlaki with pitta (what the French call a "sandwich Grecque").
Emulators have existed in astrophysics long before ML became part of the zeitgeist. The earliest cosmology emulator paper that I'm aware of is from 2009 here: https://arxiv.org/abs/0902.0429. IIRC the method came from fluid dynamics. It just so happens that the interpolators used under the hood (clasically it was GPs but is NNs latelys) are also popular in ML, and so the method gets lumped into the ML pile.
The key difference between emulation and typical ML is that emulation is always in an interpolation setting, whereas typically predictive ML is extrapolating in one way or another (e.g. predicting future events).
Congrats to the authors!
While the work the authors do is important, in no sense does the tool they produced actually run a simulation.
A simulation implies a physical model and usually partial differential equations that are often solved on supercomputers, but here the neural network is rather interpolating some fixed simulation output in a purely data-driven way.
The simulations have not gotten faster due to neural networks, cosmologists have just gotten better at using them. Which is great!
Edit: see the sub-comment in the thread by crazygringo for the lead author’s take
[1] https://arxiv.org/pdf/2509.18480 [2] https://github.com/apple/ml-simplefold
It is such a great language, and capable of ML and AI workloads, as evidenced by this research.