SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
Name
2005.08803.pdf
Description
Submitted version
Size
3.6 MB
Format
Adobe PDF
Checksum (MD5)
edceb2c96318cdb70ff9136c390f65a7
Author(s)
Haghighat, Ehsan
Juanes, Ruben
Date Issued
November 2020
Journal
Computer Methods in Applied Mechanics and Engineering
Publisher
Elsevier BV
Citation
Ehsan Haghighat, Ruben Juanes, SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks, Computer Methods in Applied Mechanics and Engineering, Volume 373, 2021
Version
Original manuscript
Abstract
© 2020 Elsevier B.V. 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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CMA.2020.113552
