## Tags - Part of: - Related: - Includes: - Additional: ## Definitions - [[Physics]] x [[Artificial Intelligence]] x [[Machine learning]] x [[Data science]] x [[Statistics]] ## Main resources - <iframe src="https://en.wikipedia.org/wiki/" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - [FermiNet: Quantum physics and chemistry from first principles - Google DeepMind](https://deepmind.google/discover/blog/ferminet-quantum-physics-and-chemistry-from-first-principles/) - [AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations | Papers With Code](https://paperswithcode.com/paper/ai-poincare-2-0-machine-learning-conservation) - [\[2403.05618\] OmniJet-$α$: The first cross-task foundation model for particle physics](https://arxiv.org/abs/2403.05618) - [\[2306.00258\] Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior](https://arxiv.org/abs/2306.00258) - [AI for Physics & Physics for AI - Max Tegmark - 4/1/21 - YouTube](https://www.youtube.com/watch?v=_I6A8e7Nfnk) - [Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning - YouTube](https://www.youtube.com/watch?v=KmQkDgu-Qp0) - Training autoencoder (machine learning architecture) to predict physical dynamical systems second in advance and then slowly reducing the size of the model without loosing precision and in the limit getting its dimensionality as number of modelled variables about the dynamical system that the network learned [Can AI disover new physics? - YouTube](https://www.youtube.com/watch?v=XRL56YCfKtA) ## Brainstorming [[Thoughts AI x physics]] ## Resources [[Links AI physics]] ## AI landscape (may include incorrect information) AI has made significant contributions to solving complex problems in physics across various areas. Here are some key ways AI has helped advance physics research: ## Data Analysis and Pattern Recognition AI excels at analyzing large datasets and identifying patterns that may be difficult for humans to detect. This capability has been particularly useful in: - Particle physics: AI algorithms help sift through massive amounts of data from particle accelerators like the Large Hadron Collider to identify rare events and potential new particles[1][4]. - Astrophysics: Machine learning techniques assist in analyzing data from telescopes and gravitational wave detectors to identify astronomical phenomena like exoplanets and black hole mergers[4][8]. ## Simulation and Modeling AI can create sophisticated simulations and models of complex physical systems: - Quantum systems: Neural networks can simulate quantum many-body systems more efficiently than traditional computational methods[1]. - Fluid dynamics: AI models help simulate and predict fluid behavior in complex scenarios[4]. ## Optimization of Experiments AI helps optimize experimental design and data collection: - Accelerator physics: Machine learning algorithms optimize particle accelerator operations and beam control[1]. - Adaptive optics: AI improves real-time adjustments in telescope systems to enhance image quality[4]. ## Solving Differential Equations AI techniques like physics-informed neural networks (PINNs) can solve complex differential equations that describe physical systems: - Heat transfer: PINNs have been used to solve heat transfer equations in advanced manufacturing applications[5]. - Fluid flow: AI methods based on PINNs have been applied to solving fluid flow problems[5]. ## Discovery of New Physical Laws While still an emerging area, researchers are exploring whether AI can help discover entirely new physical laws: - Pattern recognition in complex datasets: AI systems are being trained on vast physics datasets to potentially identify new patterns or relationships that could lead to the discovery of new physical laws[8]. - Rediscovery of known laws: AI has demonstrated the ability to rediscover fundamental equations like Newton's second law from raw data, suggesting potential for uncovering new physics[8]. While AI has made significant contributions, it's important to note that human physicists remain crucial in interpreting results, designing experiments, and developing theoretical frameworks. AI serves as a powerful tool to augment and accelerate physics research rather than replace human insight and creativity. Citations: [1] https://texta.ai/blog/ai-content/solving-complex-physics-problems-using-ai-technology [2] https://writingmate.ai/blog/ai-physics-tools [3] https://www.lindau-nobel.org/blog-can-ai-discover-new-physics/ [4] https://rescale.com/blog/ai-physics/ [5] https://www.tutorsindia.com/blog/artificial-intelligence-for-solving-physics-problems/ [6] https://tutorbin.com/physics-ai-solver [7] https://www.youtube.com/watch?v=Bs9AASPWyOU [8] https://www.webpronews.com/can-ai-discover-entirely-laws-of-physics/