Shypd.aiThe largest open index of AI toolsPINNpapers is an open-source repository that curates must-read papers on Physics-Informed Neural Networks (PINNs). It provides a categorized list of research papers, including software, models, acceleration techniques, and applications.
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PINNpapers is a comprehensive, open-source repository maintained by the IDRL lab, dedicated to curating essential research papers on Physics-Informed Neural Networks (PINNs). Since PINNs have gained significant traction in scientific computing, this resource serves as a valuable collection of representative works in the field. The repository categorizes papers across various aspects of PINNs, including foundational models, parallel computing approaches, acceleration techniques, model transfer and meta-learning, probabilistic PINNs, uncertainty quantification, and diverse applications. It also lists relevant software libraries like DeepXDE and SciANN, providing links to papers and code where available. Researchers and practitioners can use this resource to stay updated on the latest advancements and foundational concepts in PINN research.
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