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arXiv · 2603.11544

Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality

Qijia Zhou·Yiyang Wang·Shengyuan Deng·Chenliang Li
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Abstract

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.

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Qijia Zhou, Yiyang Wang, Shengyuan Deng, Chenliang Li. 2026-03-12. Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality. https://arxiv.org/abs/2603.11544

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