Shypd.aiThe largest open index of AI toolsPhysics-Informed-Neural-Networks is an open-source research repository for solving Partial Differential Equations (PDEs) by incorporating physics into neural network loss functions. It provides implementations in TensorFlow 2 and PyTorch.
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Physics-Informed-Neural-Networks (PINNs) is a research repository dedicated to investigating and implementing PINNs for solving Partial Differential Equations (PDEs). It integrates the physics of the PDE and boundary conditions directly into the neural network's loss function, utilizing the Mean-Squared Error of the PDE and boundary residual measured on 'collocation points'. The repository currently offers implementations for Burgers' and Helmholtz PDEs in both TensorFlow 2 and PyTorch. It also explores various aspects of PINNs, including the effectiveness of the L-BFGS optimizer for stiff PDEs, bottom-up learning mechanisms, and the impact of transfer learning on solution error, providing valuable insights for researchers and practitioners in scientific computing.
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