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betelgeuse3e08 t1_iv4xedo wrote

Recently there has been growing interest in developing better deep neural network based dynamics models for physical systems, through better inductive biases. Here are some papers that utilize the structure of Lagrangian / Hamiltonian mechanics to learn better dynamics models,

  • Deep Lagrangian Networks (DeLaN)
  • Hamiltonian neural networks
  • DeLaN for energy control
  • Symplectic ode-net (Symoden)
  • Dissipative symoden
  • Lagrangian neural networks
  • Simplifying hamiltonian and lagrangian neural networks via explicit constraints
  • Extending lagrangian and hamiltonian neural networks with differentiable contact models

The following survey paper nicely summarizes some of the work in this area,

  • Benchmarking energy-conserving neural networks for learning dynamics from data.
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ShadowKnightPro OP t1_iv5aqta wrote

Thank you for such an insightful comment!

However, I'm looking for research that borrows the idea of Physics to solve AI tasks(CV, NLP, ...), kinda like the Poisson flow generative model above. Do you know any papers?

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betelgeuse3e08 t1_iv5g9h6 wrote

I have been using these physics-informed dynamics models in controls / RL.

From a CV / NLP perspective, I'm not particularly sure. There was some work from Deepmind on learning latent dynamics from images. Check out "Benchmarking models for learning latent dynamics". However, I'm not sure if this is something you'd be interested in.

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canbooo t1_iv6j73z wrote

I think the comment above you is gold and you are approaching this kinda wrong if this is about research. The fact that they are not (yet) solving cv/nlp tasks is an advantage rather than a disadvantage. Although I must admit, I see a more direct relation to RL than anything, this makes it even more interesting since any idea you will come up with will probably be novel.

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ShadowKnightPro OP t1_iv94h1t wrote

I totally agree with you, but I'm still in undergrad (just submitted one paper about multi-modal) and I have to prepare for grad school, aiming for top uni. Thus, I suppose publishing more on trendy subfields would benefit more (Maybe I'm wrong on this). Probably I will work on them in my Ph.D. Thanks for your advice!

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canbooo t1_iv9jjg9 wrote

Ok you are right, I was assuming you are already doing your PhD. In this case, I would keep it simple and focus on methodology rather than novelty. Good luck with your search and thesis.

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