[cameronperot]

I'm studying in the intersection of physics and data science, and I think there's a number of places where physics can benefit from ML. From my current point of view though, most of these applications lie more on the experimental/computational sides of physics rather than the theoretical side. One of the current use cases is using ML to aid in the processing and analysis of data obtained from experiments.

I would like to see more truly innovative work done on the theoretical side, but I don't think we'll see "AI" bridge the gap between QFT and GR any time soon. I think in order for something like that to happen we need a new approach, as the current approach of throwing deep learning models at it doesn't feel like the right answer.

On a more general note, the SciML organization [1] has been quite successful in helping incorporating more ML into science.

[1] https://sciml.ai/

[md2020]

I agree that the potential impact of ML on the theoretical side is very exciting. I think there’s a lot of bridging to be done between the most advanced mathematics and the most advanced physics that could lead to new insight, but it’s a hard problem for humans to tackle since we have very few people who are deeply proficient in both—although it is becoming more common. I’m thinking something like GPT-3 trained on literature in both fields could be the kind of thing we want, but like you I still doubt that a DL system is likely to come up with any real insight. I’d like to be proven wrong, though.

[spyder]

GPT-3 is already not too bad with basic physics:

https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-c...

And this is without training on the specific task. It's getting scary...

[p1esk]

On the theoretical side, ML can be used to find a conceptual pattern in the existing literature. E.g. here's a paragraph describing a novel idea, go read all physics (and beyond) papers and find those that describe similar ideas.

[ylem]

Really cool! What problem are you working on? I live on the experimental side. At least in condensed matter, there are people having fun on the theory side as well.

[cameronperot]

I'm not working on any specific problem yet, but for my master's thesis I'm hoping to do something related to the use of neural networks in numerical solutions to differential equations. Along the lines of this sort of stuff [2].

[2] https://arxiv.org/pdf/2001.04385.pdf

[ylem]

There were some interesting talks on neural differentiation applied to physics at ICLR. You probably saw: https://arxiv.org/abs/1906.01563

Very fun!

[cameronperot]

I came across that paper just recently, it was a very good read!