IDRLnet: A Physics-Informed Neural Network Library

• URL: http://arxiv.org/abs/2107.04320v1
• Date: Fri, 9 Jul 2021 09:18:35 GMT
• Title: IDRLnet: A Physics-Informed Neural Network Library
• Authors: Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, Xiaoqian Chen
• Abstract summary: Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems. This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically.
• Score: 9.877979064734802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically. IDRLnet constructs the framework for a wide range of PINN algorithms and applications. It provides a structured way to incorporate geometric objects, data sources, artificial neural networks, loss metrics, and optimizers within Python. Furthermore, it provides functionality to solve noisy inverse problems, variational minimization, and integral differential equations. New PINN variants can be integrated into the framework easily. Source code, tutorials, and documentation are available at \url{https://github.com/idrl-lab/idrlnet}.

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