Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)

• URL: http://arxiv.org/abs/2406.03172v1
• Date: Wed, 5 Jun 2024 12:03:45 GMT
• Title: Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)
• Authors: Chenhao Si, Ming Yan,
• Abstract summary: We propose a new physics-informed neural network framework, IDPINN, to improve prediction accuracy. We numerically evaluated it on several forward problems and demonstrated the benefits of IDPINN in terms of accuracy.
• Score: 14.65008276932511
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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