  Physics-Informed Neural Networks promise to revolutionize science and
engineering practice, by introducing domain-aware deep machine learning models
into scientific computation. Several software suites have emerged to make the
implementation and usage of these architectures available to the research and
industry communities. Here we introduce\linebreak TensorDiffEq, built on
Tensorflow 2.x, which presents an intuitive Keras-like interface for problem
domain definition, model definition, and solution of forward and inverse
problems using physics-aware deep learning methods. TensorDiffEq takes full
advantage of Tensorflow 2.x infrastructure for deployment on multiple GPUs,
allowing the implementation of large high-dimensional and complex models.
Simultaneously, TensorDiffEq supports the Keras API for custom neural network
architecture definitions. In the case of smaller or simpler models, the package
allows for rapid deployment on smaller-scale CPU platforms with negligible
changes to the implementation scripts. We demonstrate the basic usage and
capabilities of TensorDiffEq in solving forward, inverse, and data assimilation
problems of varying sizes and levels of complexity. The source code is
available at https://github.com/tensordiffeq.
