Standard Model is extremely well tested, but e.g. is incompatible with general relativity, is rather for effective perturbative approximations, also uses this gigantic Lagrangian found in epicycle-style: by guess&fit terms.

So maybe like in Copernican Revolution, we should search for compact deeper nonperturbative Lagrangian (e.g. Skyrme-like), effectively described close to Standard Model + gravity?

Nonperturbative Lagrangians look simple, but have extremely complex consequences - maybe AI could search through them, automatically performing simulations testing various agreements?

This kind of approaches have already started, e.g.:
Towards AI-assisted neutrino flavor theory design": https://www.nature.com/articles/s42005-026-02627-2
"Agentic Exploration of Physics Models" https://journals.aps.org/prx/abstract/10.1103/xnqc-q6nt
https://github.com/openwave-labs/openwave/blob/main/MODELS.md actually testing such deeper Lagrangian candidates - currently winning is liquid-crystal-like: just assumption that field has preferred anisotropy.

What do you think about such approaches?
Where to search for such deeper Lagrangian?

Edited July 16Jul 16 by Duda Jarek

5 hours ago, Duda Jarek said:

I don't recall where I saw it or when, but I do remember seeing a quote saying that a neural network is the second-best way to solve any problem. The above diagram kind of reminds me of what a second-best solution would look like.

I was also skeptical, but recently work with Fable: multiple agents writing&performing simulations, asking questions, planning new tasks, discussing ... only sometimes asking human for suggestions, but it is becoming less and less frequent :/
What more can human do?
E.g. yesterday report: https://github.com/openwave-labs/openwave/blob/main/openwave/xperiments/m5_liquid_crystal/research/findings/m5_20_3_method_note.md

ps. or fresh Fable simulations of muon/taon decay into electron (hedgehog of one of 3 axes in 3D), releasing energy difference into neutrinos as topological vortex loops: https://github.com/openwave-labs/openwave/blob/main/openwave/xperiments/m5_liquid_crystal/research/findings/m5_21_6_note.md

Edited July 19Jul 19 by Duda Jarek

(How and why) could AI (help to) find deeper physics? - talk: https://www.youtube.com/watch?v=RSqIUWnnvqA

Current physics works on particles as abstract perfect points - what is only effective perturbative approximation. Deeper there are complex field configurations, mainstream doesn't understand - e.g. angular momentum of electron.

Many candidates for deeper Lagrangians were proposed, but understanding their consequences need extremely difficult simulations - now top LLM models can automatically write, run, analyze - what is already leading to rigorous tests, improvements, maybe search by AI - to finally get one level deeper in our physics understanding, what will have huge consequences.

Moderator Note

Posting to advertise your youtube channel is a violation of rule 2.7

Just wanted to stimulate discussion on the topic in title, e.g. by gathering my thoughts on it in this talk.

Here is a mainstream lecture "Physics-Informed Machine Learning – Lecture 1 | Why Physics + AI? ": https://www.youtube.com/watch?v=BVkGy4oz3Lg