Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909760098402304 |
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| author | Jin, Bangti Wu, Longjun |
| author_facet | Jin, Bangti Wu, Longjun |
| contents | Physics informed neural networks (PINNs) represent a very popular class of neural solvers for partial differential equations. In practice, one often employs stochastic gradient descent type algorithms to train the neural network. Therefore, the convergence guarantee of stochastic gradient descent is of fundamental importance. In this work, we establish the linear convergence of stochastic gradient descent / flow in training over-parameterized two layer PINNs for a general class of activation functions in the sense of high probability. These results extend the existing result [18] in which gradient descent was analyzed. The challenge of the analysis lies in handling the dynamic randomness introduced by stochastic optimization methods. The key of the analysis lies in ensuring the positive definiteness of suitable Gram matrices during the training. The analysis sheds insight into the dynamics of the optimization process, and provides guarantees on the neural networks trained by stochastic algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_21571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks Jin, Bangti Wu, Longjun Machine Learning Numerical Analysis Physics informed neural networks (PINNs) represent a very popular class of neural solvers for partial differential equations. In practice, one often employs stochastic gradient descent type algorithms to train the neural network. Therefore, the convergence guarantee of stochastic gradient descent is of fundamental importance. In this work, we establish the linear convergence of stochastic gradient descent / flow in training over-parameterized two layer PINNs for a general class of activation functions in the sense of high probability. These results extend the existing result [18] in which gradient descent was analyzed. The challenge of the analysis lies in handling the dynamic randomness introduced by stochastic optimization methods. The key of the analysis lies in ensuring the positive definiteness of suitable Gram matrices during the training. The analysis sheds insight into the dynamics of the optimization process, and provides guarantees on the neural networks trained by stochastic algorithms. |
| title | Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2508.21571 |