<p>N-body simulations essentially map the parameters of the cosmological model onto present day observables (e.g. the halo mass function, halo density profiles, star formation rates and so on).
Say you want to know how a certain observable <span class="math-container">$\mathcal{O}$</span> depends on a set of model parameters <span class="math-container">$\lambda_1, ..., \lambda_n$</span>. To determine <span class="math-container">$\mathcal{O}(\lambda_1,\dots,\lambda_n)$</span>, you would require lots of N-body simulations, because you need one for every possible combination of parameters. This is problematic, because realistic cosmological N-body simulations are computationally expensive.</p>
<p>So we might be able to feed AI models with the data from a few simulations with different parameters and have it learn the functional dependence <span class="math-container">$\mathcal{O}(\lambda_1,\dots,\lambda_n)$</span>.</p>
<p>Another thing people are trying is to model structure formation with explainable AI models. The hope here is that we can actually understand how the AI is producing its output and from that gain some knowledge about the underlying physics. Here's a nice talk on this topic: <div class="youtube-embed"><div>
                <iframe width="640px" height="395px" src="https://www.youtube.com/embed/GMXxbmXapsw?start=0"></iframe>
            </div></div></p>
<p>(Note that I'm not an expert on this and I only started learning about it recently, so take this with a grain of salt.)</p>

CC BY-SA 4.0
2024-11-14T20:22:55.807Z
2024-11-14T20:33:11.157Z
2024-11-14T20:33:11.157Z
Lenard Kasselmann
<p>People use N-body simulations to compute Large Scale Structure (LSS). To use this method you need to make assumptions about the underlying physics, e.g. the parameters of the Universe expansion, the nature of dark matter and so on.</p>
<p>On the other hand, there are approximate methods to simulate LSS, e.g. generative neural networks. They don't rely on physics and only imitate N-body simulation output.</p>
<p><strong>My question is:</strong> what are real-world examples of use of approximate methods for LSS simulation? I've googled and searched in articles but all examples seem a little vague to me.</p>
<p>Could you please describe any physics task when we can make use of approximate simulations?</p>

CC BY-SA 4.0
2024-11-14T19:04:42.693Z
Could you link to some of those articles that use neural networks to simulate the LSS?
pela
CC BY-SA 4.0
2024-11-14T19:10:20.560Z
@pela for example here: https://link.springer.com/article/10.1186/s40668-019-0032-1
dust
CC BY-SA 4.0
2024-11-14T19:23:49.513Z
Maybe my confusion comes from the fact that I don't understand how exactly original LSS structure simulations are used. In my mind we just simulate something and then compare it with observations (no specific examples). From my experience with deep learning, I understand that in case of neutral network simulation the goal is to get much data similar to train data (LSS from physical N-body methods) but very fast and maybe not super accurate. Not sure what physical task it solves though
dust
CC BY-SA 4.0
2024-11-14T20:44:58.867Z
You understanding of cosmological simulations is more or less correct, and also of how NNs are used, I think, although I only have experience with the former. Lenard's answer below seems to address your question well, if I understand correctly (but could perhaps use some more detail).
pela
CC BY-SA 4.0
2024-11-15T01:11:15.640Z
You may enjoy the answers & links in this related question: https://astronomy.stackexchange.com/q/55010/16685
PM 2Ring
CC BY-SA 4.0
2024-11-15T05:17:29.903Z
@PM2Ring Yes I've seen this. The answers to this question are more about the generation itself and not the purpose of it, but digging into links at this page could be helpful, thanks
dust
<cosmology><n-body-simulations><physics-simulations><neural-networks>
What is the point of approximate Large Scale Structure simulation with neural networks?
dust
# What is the point of approximate Large Scale Structure simulation with neural networks?

(Asked by: username_0 on 2024-11-14)

People use N-body simulations to compute Large Scale Structure (LSS). To use this method you need to make assumptions about the underlying physics, e.g. the parameters of the Universe expansion, the nature of dark matter and so on.

On the other hand, there are approximate methods to simulate LSS, e.g. generative neural networks. They don't rely on physics and only imitate N-body simulation output.

**My question is:** what are real-world examples of use of approximate methods for LSS simulation? I've googled and searched in articles but all examples seem a little vague to me.

Could you please describe any physics task when we can make use of approximate simulations?

**Tags:** cosmology, n-body-simulations, physics-simulations, neural-networks

> **Comments:**
> **username_1**: Could you link to some of those articles that use neural networks to simulate the LSS?  
> **username_0**: @username_1 for example here: https://link.springer.com/article/10.1186/s40668-019-0032-1  
> **username_0**: Maybe my confusion comes from the fact that I don't understand how exactly original LSS structure simulations are used. In my mind we just simulate something and then compare it with observations (no specific examples). From my experience with deep learning, I understand that in case of neutral network simulation the goal is to get much data similar to train data (LSS from physical N-body methods) but very fast and maybe not super accurate. Not sure what physical task it solves though  
> **username_1**: You understanding of cosmological simulations is more or less correct, and also of how NNs are used, I think, although I only have experience with the former. username_3's answer below seems to address your question well, if I understand correctly (but could perhaps use some more detail).  
> **username_2**: You may enjoy the answers & links in this related question: https://astronomy.stackexchange.com/q/55010/16685  
> **username_0**: @username_2 Yes I've seen this. The answers to this question are more about the generation itself and not the purpose of it, but digging into links at this page could be helpful, thanks  

---

## 1 answers

### Answer by username_3 on 2024-11-14. Score: 2

N-body simulations essentially map the parameters of the cosmological model onto present day observables (e.g. the halo mass function, halo density profiles, star formation rates and so on).
Say you want to know how a certain observable $\mathcal{O}$ depends on a set of model parameters $\lambda\_1, ..., \lambda\_n$. To determine $\mathcal{O}(\lambda\_1,\dots,\lambda\_n)$, you would require lots of N-body simulations, because you need one for every possible combination of parameters. This is problematic, because realistic cosmological N-body simulations are computationally expensive.

So we might be able to feed AI models with the data from a few simulations with different parameters and have it learn the functional dependence $\mathcal{O}(\lambda\_1,\dots,\lambda\_n)$.

Another thing people are trying is to model structure formation with explainable AI models. The hope here is that we can actually understand how the AI is producing its output and from that gain some knowledge about the underlying physics. Here's a nice talk on this topic:

(Note that I'm not an expert on this and I only started learning about it recently, so take this with a grain of salt.)


---

