Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914387562856448 |
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| author | Zhou, Qijia Wang, Yiyang Deng, Shengyuan Li, Chenliang |
| author_facet | Zhou, Qijia Wang, Yiyang Deng, Shengyuan Li, Chenliang |
| contents | Variational inequalities are widely applied in mechanical engineering, fluid penetration, transportation, and other fields. In this paper, a Deep Ritz method based on Physics-Informed Neural Networks (PINNs) is proposed to enhance the accuracy and efficiency of solving elliptic variational inequalities. The Ritz variational method is firstly utilized to transform the variational inequality problem into an optimization problem. Then Bayesian optimization is employed to tune the weights of the loss function, and a residual-based adaptive dataset update strategy is introduced to improve the convergence and accuracy of the model. Numerical experiments show that the proposed method can effectively approximate the analytical solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11544 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality Zhou, Qijia Wang, Yiyang Deng, Shengyuan Li, Chenliang Numerical Analysis Variational inequalities are widely applied in mechanical engineering, fluid penetration, transportation, and other fields. In this paper, a Deep Ritz method based on Physics-Informed Neural Networks (PINNs) is proposed to enhance the accuracy and efficiency of solving elliptic variational inequalities. The Ritz variational method is firstly utilized to transform the variational inequality problem into an optimization problem. Then Bayesian optimization is employed to tune the weights of the loss function, and a residual-based adaptive dataset update strategy is introduced to improve the convergence and accuracy of the model. Numerical experiments show that the proposed method can effectively approximate the analytical solution. |
| title | Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2603.11544 |