DeepXDE: A Deep Learning Library for Solving Differential Equations by Lu Lu

Lu Lu (Brown), "DeepXDE: A Deep Learning Library for Solving Differential Equations" 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. PINNs solve inverse problems similarly to forward problems. We also present a Python library for PINNs, DeepXDE. DeepXDE supports complex-geometry domains based on the technique of constructive solid geometry, and enables the user code to be compact, resembling closely the mathematical formulation. We introduce the usage of DeepXDE, and we also demonstrate the capability of PINNs and the user-friendliness of DeepXDE for two different examples. DeepXDE is available at https://github.com/lululxvi/deepxde AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 23-25, 2020 (https://sites.google.com/view/aaai-mlps) Papers: https://sites.google.com/view/aaai-ml... Slides: https://sites.google.com/view/aaai-ml...