Shypd.aiThe largest open index of AI toolsPINNs is an open-source tool that utilizes physics-informed neural networks for solving supervised learning tasks. It is designed for data-driven solutions and discovery of nonlinear partial differential equations.
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PINNs (Physics Informed Neural Networks) is an open-source deep learning framework designed to solve supervised learning tasks while adhering to physical laws described by nonlinear partial differential equations. It offers capabilities for both data-driven solution and data-driven discovery of PDEs. The tool supports continuous time and discrete time models, forming a class of data-efficient universal function approximators that embed underlying physical laws as prior information. PINNs can infer solutions to PDEs, create physics-informed surrogate models, and facilitate the discovery of partial differential equations from data. While the original repository is no longer actively maintained, the underlying concepts are widely implemented in PyTorch, JAX, and TensorFlow v2.
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