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.

中文翻译：

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SciANN：使用人工神经网络进行科学计算和基于物理的深度学习的 Keras/Tensorflow 包装器

在本文中，我们介绍了 SciANN，这是一个 Python 包，用于使用人工神经网络进行科学计算和基于物理的深度学习。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以进行迁移学习。SciANN 旨在使用物理信息神经网络 (PINN) 架构抽象神经网络构造，以用于科学计算和偏微分方程 (PDE) 的求解和发现，因此提供了设置复杂函数形式的灵活性。我们在一系列示例中说明了该框架如何用于离散数据的曲线拟合，以及如何解决和发现强和弱形式的偏微分方程。