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| Physics-Based Deep Learning v4 (arxiv.org) | |
| 5 points by __rito__ on Mar 28, 2025 | hide | past | pdf | 1 comment on HN | |
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In plain words: A hands-on guide teaches deep learning for physical simulations, pairing every concept with interactive notebooks so readers can run the code themselves. It covers training on data, baking physics rules into the training signal, and making simulations differentiable, alongside generative and reinforcement learning. Abstract · Physics-based Deep LearningThis document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly. Beyond traditional supervised learning, we dive into physical loss-constraints, differentiable simulations, diffusion-based approaches for probabilistic generative AI, as well as reinforcement learning and advanced neural network architectures. These foundations are paving the way for the next generation of scientific foundation models. We are living in an era of rapid transformation. These methods have the potential to redefine what's possible in computational science. | |
| add comment on HN |
This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly. Beyond traditional supervised learning, we dive into physical loss-constraints, differentiable simulations, diffusion-based approaches for probabilistic generative AI, as well as reinforcement learning and advanced neural network architectures. These foundations are paving the way for the next generation of scientific foundation models. We are living in an era of rapid transformation. These methods have the potential to redefine what's possible in computational science.