# DeepXDE: A Deep Learning Library for Solving Differential Equations

@article{Lu2020DeepXDEAD, title={DeepXDE: A Deep Learning Library for Solving Differential Equations}, author={Lu Lu and X. Meng and Zhiping Mao and G. Karniadakis}, journal={SIAM Rev.}, year={2020}, volume={63}, pages={208-228} }

Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present an overview of physics-informed neural networks (PINNs), which embed a PDE into the loss of the neural network using automatic differentiation. The PINN algorithm is simple, and it can be applied to different types of PDEs, including integro-differential equations, fractional PDEs, and stochastic PDEs. Moreover, from… Expand

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