Shypd.aiThe largest open index of AI toolspbdl-book is an open-source Research & Education tool that provides a comprehensive guide to physics-based deep learning. It emphasizes practical application with interactive Jupyter notebooks and covers advanced topics like generative AI and differentiable simulations.
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The Physics-based Deep Learning book (PBDL) v0.3 is an open-source resource offering a hands-on, comprehensive guide to deep learning within physical simulations. It moves beyond theoretical concepts, focusing on practical application with interactive Jupyter notebooks for every concept. The book delves into physical loss-constraints, differentiable simulations, diffusion-based approaches for probabilistic generative AI, reinforcement learning, and advanced neural network architectures. This latest edition, v0.3, includes a major new chapter on generative modeling, covering techniques like denoising, flow-matching, and physics-integrated constraints, along with a dedicated section on neural architectures for physics simulations. All code examples are updated to leverage the latest frameworks, making it a valuable resource for those looking to apply deep learning to solve PDE problems and combine it with existing physics knowledge.
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