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