Shypd.aiThe largest open index of AI toolsPhysics-Based-Deep-Learning is an Open Source research tool that provides a curated collection of links to works on deep learning algorithms for physics problems, with a focus on fluid dynamics. It serves as a valuable resource for researchers and developers in the field.
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Physics-Based-Deep-Learning is an open-source GitHub repository that compiles an extensive list of research papers and projects focused on the intersection of physical modeling and deep learning techniques. This resource is particularly valuable for those interested in "Physics-Based Deep Learning" (PBDL), covering methods that combine physical simulations with artificial neural networks. The repository categorizes approaches based on whether they target forward simulations or inverse problems, and by the integration type between learning and physics, including data-driven, loss-term, and interleaved methods. It emphasizes fluid flow and Navier-Stokes related problems, primarily featuring works from the I15 lab at TUM, alongside contributions from other research groups. This collection is ideal for academic research and staying updated on advancements in differentiable physics and AI for scientific computing.
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