Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917076586725376 |
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| author | Rahman, Mohammad Mahabubur Verma, Deepanshu |
| author_facet | Rahman, Mohammad Mahabubur Verma, Deepanshu |
| contents | Due to its nonlinearity, bi-harmonic dissipation, and backward heat-like term in the absence of a divergence-free condition, the $2$-D/$3$-D Kuramoto-Sivashinsky equation poses significant challenges for both mathematical analysis and numerical approximation. These difficulties motivate the development of methods that blend classical analysis with numerical approximation approaches embodied in the framework of the physics-informed neural networks (PINNs). In addition, despite the extensive use of PINN frameworks for various linear and nonlinear PDEs, no study had previously established rigorous error estimates for the Kuramoto-Sivashinsky equation within a PINN setting. In this work, we overcome the inherent challenges, and establish several global regularity criteria based on space-time integrability conditions in Besov spaces. We then derive the first rigorous error estimates for the PINNs approximation of the Kuramoto-Sivashinsky equation and validate our theoretical error bounds through numerical simulations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_09728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation Rahman, Mohammad Mahabubur Verma, Deepanshu Numerical Analysis Due to its nonlinearity, bi-harmonic dissipation, and backward heat-like term in the absence of a divergence-free condition, the $2$-D/$3$-D Kuramoto-Sivashinsky equation poses significant challenges for both mathematical analysis and numerical approximation. These difficulties motivate the development of methods that blend classical analysis with numerical approximation approaches embodied in the framework of the physics-informed neural networks (PINNs). In addition, despite the extensive use of PINN frameworks for various linear and nonlinear PDEs, no study had previously established rigorous error estimates for the Kuramoto-Sivashinsky equation within a PINN setting. In this work, we overcome the inherent challenges, and establish several global regularity criteria based on space-time integrability conditions in Besov spaces. We then derive the first rigorous error estimates for the PINNs approximation of the Kuramoto-Sivashinsky equation and validate our theoretical error bounds through numerical simulations. |
| title | Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2511.09728 |