Work

Initialization-enhanced physics-informed neural network with domain decomposition (IDPINN)

Year: 2025

Type: article

Abstract: We propose a new physics-informed neural network framework, IDPINN, based on the enhancement of initialization and domain decomposition to improve prediction accuracy. We train a PINN using a small dataset to obtain an initial network structure, including the weighted matrix and bias, which initializes the PINN for each subdomain. Moreover, we leverage the smoothness condition on the interface to enhance the prediction performance. We numerically evaluated it on several forward problems and demonstrated the benefits of IDPINN in terms of accuracy.

Source: Journal of Computational Physics

DOI: 10.1016/j.jcp.2025.113914

Authors: Chenhao Si, Ming Yan

Institutions: Chinese University of Hong Kong, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,980

Funder: National Natural Science Foundation of China