论文标题
SCIANN：使用人工神经网络的科学/张量化包装器，用于科学计算和物理知识深度学习
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
论文作者
论文摘要
在本文中，我们介绍了Sciann，这是一种使用人工神经网络的科学计算和物理知识深度学习的Python软件包。 Sciann使用广泛使用的深度学习软件包Tensorflow和Keras来构建深层神经网络和优化模型，从而继承了Keras的许多功能，例如批处理优化和转移学习的模型重复使用。 SCIANN旨在使用物理知识的神经网络（PINN）体系结构来抽象神经网络构建，用于科学计算和解决方案和发现部分微分方程（PDE），因此为建立复杂的功能形式提供了灵活性。我们在一系列示例中说明了如何将框架用于离散数据上的曲线拟合，以及以强和弱形式以解决方案和发现PDE。我们总结了Sciann当前可用的功能，还概述了正在进行的和未来的发展。
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.
