Shypd.aiThe largest open index of AI toolsDeepXDE is a library for scientific machine learning and physics-informed learning. It provides algorithms for solving differential equations using neural networks and supports multiple backend tensor libraries.
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DeepXDE is a comprehensive open-source library designed for scientific machine learning and physics-informed learning. It offers a wide array of algorithms, including physics-informed neural networks (PINN) for solving various types of differential equations (ODEs, PDEs, IDEs, fPDEs, sPDEs), and deep operator networks (DeepONet) for learning operators. The library supports five tensor libraries as backends: TensorFlow 1.x, TensorFlow 2.x, PyTorch, JAX, and PaddlePaddle. DeepXDE is highly configurable, allowing users to define complex domain geometries, various boundary conditions, and different neural network architectures. It also includes features like adaptive sampling, gradient-enhanced PINNs, and multifidelity learning, making it a versatile tool for researchers and engineers in scientific computing.
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