SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
复制标题
SciANN：一个Keras/TensorFlow包装器，用于使用人工神经网络进行科学计算和物理深度学习
DOI：
10.1016/j.cma.2020.113552
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发表时间：
2021-01-01
影响因子：
7.2
通讯作者：
Juanes, Ruben
中科院分区：
文献类型：
--
作者：
Haghighat, Ehsan;Juanes, Ruben
在本文中，我们介绍了SciANN，这是一个使用人工神经网络进行科学计算和物理深度学习的Python包。SciANN使用广泛使用的深度学习包TensorFlow和Keras来构建深度神经网络和优化模型，从而继承了Keras的许多功能，例如批量优化和模型重用。SciANN旨在使用物理信息神经网络（PINN）架构抽象神经网络结构，用于科学计算和偏微分方程（PDE）的求解和发现，从而提供设置复杂函数形式的灵活性。我们说明，在一系列的例子中，该框架可以用于曲线拟合离散数据，并在强和弱形式的偏微分方程的解决方案和发现。我们总结了SciANN目前可用的功能，并概述了正在进行的和未来的发展。(C)2020爱思唯尔B.V.保留所有权利。
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.