☆ 4.7 Article

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

期刊

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

卷 373, 期 -, 页码 -

出版社

ELSEVIER SCIENCE SA

DOI: 10.1016/j.cma.2020.113552

关键词

SciANN; Deep neural networks; Scientific computations; PINN; vPINN

类别

Engineering, Multidisciplinary Mathematics, Interdisciplinary Applications Mechanics

资金

KFUPM-MIT, United States, collaborative agreement 'Multiscale Reservoir Science'

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Protocol

Reagent

智能总结 New
摘要

SciANN is a Python package for scientific computing and physics-informed deep learning. It utilizes TensorFlow and Keras to build deep neural networks and optimization models, allowing for solving partial differential equations with flexibility in setting complex functional forms.

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. (C) 2020 Elsevier B.V. All rights reserved.

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