Can AI disover new physics? [https://www.youtube.com/watch?v=XRL56YCfKtA](https://www.youtube.com/watch?v=XRL56YCfKtA) [Learning by machines, for machines: Artificial Intelligence in the world's largest particle detector | ATLAS Experiment at CERN](https://atlas.cern/Updates/Feature/Machine-Learning) Resources for state of the art techniques in AI for fundamental physics https://grok.com/share/bGVnYWN5_800708de-0c3c-4b87-9c28-690cf4466a3e [How can AI help physicists search for new particles? | CERN](https://home.cern/news/news/physics/how-can-ai-help-physicists-search-new-particles) [Provably exact artificial intelligence for nuclear and particle physics » MIT Physics](https://physics.mit.edu/news/provably-exact-artificial-intelligence-for-nuclear-and-particle-physics/) [Symmetry’s guide to AI in particle physics and astrophysics | symmetry magazine](https://www.symmetrymagazine.org/article/symmetrys-guide-to-ai-in-particle-physics-and-astrophysics?language_content_entity=und) [[1905.01023] Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning](https://arxiv.org/abs/1905.01023) [Machine Learning Accelerates Cosmological Simulations - News - Carnegie Mellon University](https://www.cmu.edu/news/stories/archives/2021/may/machine-learning-cosmology.html) AI for particle physics in CERN CMS develops new AI algorithm to detect anomalies [CMS develops new AI algorithm to detect anomalies | CERN](https://home.cern/news/news/experiments/cms-develops-new-ai-algorithm-detect-anomalies) [AlphaQubit: Google’s research on quantum error correction](https://blog.google/technology/google-deepmind/alphaqubit-quantum-error-correction/) [Machine learning in physics - Wikipedia](https://en.wikipedia.org/wiki/Machine_learning_in_physics) [https://youtu.be/MO6ZvA7U3F0?si=qL_zKlya8vqDC5jD](https://youtu.be/MO6ZvA7U3F0?si=qL_zKlya8vqDC5jD) [[2302.04919] Variational Benchmarks for Quantum Many-Body Problems](https://arxiv.org/abs/2302.04919) Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later! [https://www.youtube.com/watch?v=NxAn0oglMVw](https://www.youtube.com/watch?v=NxAn0oglMVw) https://www.pnas.org/doi/10.1073/pnas.1517384113 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.133.233601 [Machine learning in physics - Wikipedia](https://en.wikipedia.org/wiki/Machine_learning_in_physics) [https://youtu.be/-zrY7P2dVC4?si=8USsUS0H_U2X-I4r](https://youtu.be/-zrY7P2dVC4?si=8USsUS0H_U2X-I4r) Physics Informed Neural Networks (PINNs) Adds physics bias to the loss function to penalize the model for violating physics, e.g. the divergence of the velocity field should be 0 in incompressible Navier-Stokes equations in fluid dynamics. [https://youtu.be/AEOcss20nDA?si=o518qTiUb6PPx0_6](https://youtu.be/AEOcss20nDA?si=o518qTiUb6PPx0_6) [https://www.youtube.com/watch?v=HLUIx6FqAvg](https://www.youtube.com/watch?v=HLUIx6FqAvg) [[2003.04630] Lagrangian Neural Networks](https://arxiv.org/abs/2003.04630) [[1906.01563] Hamiltonian Neural Networks](https://arxiv.org/abs/1906.01563) learning Langrangians or Hamiltonians using NNs and enforcing them in loss function to conserve energy more and they actually do much better, i love it Physicist Mario Krenn uses artificial intelligence to inspire and accelerate scientific progress. He runs the Artificial Scientist Lab at the Max Planck Institute for the Science of Light, where he develops machine-learning algorithms that discover new experimental techniques at the frontiers of physics and microscopy. He also develops algorithms that predict and suggest personalized research questions and ideas. [https://www.youtube.com/watch?v=T_2ZoMNzqHQ](https://www.youtube.com/watch?v=T_2ZoMNzqHQ) [https://youtu.be/XRL56YCfKtA?si=cEepwmpkRH9hYAfZ](https://youtu.be/XRL56YCfKtA?si=cEepwmpkRH9hYAfZ) . Dělají tam throttling velikosti latentního prostoru autoencoderu na najíti kolik variables je asi potřeba na modelování různých dynamických systémů ve fyzice, včetně těch pro který nevíme ground truth množství variables a rovnice, a dimenze toho latentního prostoru celkem odpovídá tomu kolik variables používáme my když modelujeme ty systémy podle rovnic ve fyzice, plus to nachází nějakou velikost latentního prostoru i u systémů u kterých nemáme ground truth rovnice, ze kterých by ty rovnice možná šly nějak odvodit. We present Panda: a foundation model for nonlinear dynamics pretrained on 20,000 chaotic ODE discovered via evolutionary search. Panda zero-shot forecasts unseen ODE best-in-class, and can forecast PDE despite having never seen them during training (1/8) https://x.com/wgilpin0/status/1925164094010609809