Abstract
In this paper, we introduce SciANN, a Python package for scientific computing
and physics-informed deep learning using artificial neural networks. SciANN
uses the widely used deep-learning packages Tensorflow and Keras to build deep
neural networks and optimization models, thus inheriting many of Keras's
functionalities, such as batch optimization and model reuse for transfer
learning. SciANN is designed to abstract neural network construction for
scientific computations and solution and discovery of partial differential
equations (PDE) using the physics-informed neural networks (PINN) architecture,
therefore providing the flexibility to set up complex functional forms. We
illustrate, in a series of examples, how the framework can be used for curve
fitting on discrete data, and for solution and discovery of PDEs in strong and
weak forms. We summarize the features currently available in SciANN, and also
outline ongoing and future developments.
Users
Please
log in to take part in the discussion (add own reviews or comments).