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Recently, a lot of papers proposed to use neural networks to approximatelysolve partial differential equations (PDEs). Yet, there has been a lack offlexible framework for convenient experimentation. In an attempt to fill thegap, we introduce a PyDEns-module open-sourced on GitHub. Coupled withcapabilities of BatchFlow, open-source framework for convenient andreproducible deep learning, PyDEns-module allows to 1) solve partialdifferential equations from a large family, including heat equation and waveequation 2) easily search for the best neural-network architecture among thezoo, that includes ResNet and DenseNet 3) fully control the process ofmodel-training by testing different point-sampling schemes. With that in mind,our main contribution goes as follows: implementation of a ready-to-use andopen-source numerical solver of PDEs of a novel format, based on neuralnetworks.

PyDEns: a Python Framework for Solving Differential Equations with Neural Networks