PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
This provides a tool for researchers and practitioners in computational science and engineering to apply machine learning methods to differential equations, but it is incremental as it packages existing methods.
The authors introduced PinnDE, an open-source Python library for solving differential equations using physics-informed neural networks (PINNs) and deep operator networks (DeepONets), demonstrating its effectiveness in approximating solutions through worked examples.
In recent years the study of deep learning for solving differential equations has grown substantially. The use of physics-informed neural networks (PINNs) and deep operator networks (DeepONets) have emerged as two of the most useful approaches in approximating differential equation solutions using machine learning. Here, we introduce PinnDE, an open-source Python library for solving differential equations with both PINNs and DeepONets. We give a brief review of both PINNs and DeepONets, introduce PinnDE along with the structure and usage of the package, and present worked examples to show PinnDE's effectiveness in approximating solutions of systems of differential equations with both PINNs and DeepONets.