A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network

In this article, we develop a hybrid physics-informed neural network (hybrid PINN) for partial differential equations (PDEs). We borrow the idea from the convolutional neural network (CNN) and finite volume methods. Unlike the physics-informed neural network (PINN) and its variations, the method pro...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-10, Vol.33 (10), p.5514-5526
1. Verfasser:	Fang, Zhiwei
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Approximation Artificial neural networks Convergence Convolutional neural network (CNN) Differential equations Finite volume method finite-difference method Geometry Handheld computers hybrid physics-informed neural network (hybrid PINN) Inverse problems Learning systems local fitting method Machine learning Mathematical analysis Mathematical models Neural networks Numerical methods Operators (mathematics) Partial differential equations Physics Training
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