Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918122136535040 |
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| author | Hillebrecht, Birgit Unger, Benjamin |
| author_facet | Hillebrecht, Birgit Unger, Benjamin |
| contents | Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equations, several approaches have been developed to quantify the associated prediction error. In this work, we build upon a semigroup-based framework previously introduced by the authors for estimating the PINN error. While this estimator has so far been limited to academic examples - due to the need to compute quantities related to input-to-state stability - we extend its applicability to a significantly broader class of problems. This is accomplished by modifying the error bound and proposing numerical strategies to approximate the required stability parameters. The extended framework enables the certification of PINN predictions in more realistic scenarios, as demonstrated by a numerical study of Stokes flow around a cylinder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction error certification for PINNs: Theory, computation, and application to Stokes flow Hillebrecht, Birgit Unger, Benjamin Numerical Analysis Machine Learning 65N15, 47D06, 35A35, 35F16, 41A65 Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equations, several approaches have been developed to quantify the associated prediction error. In this work, we build upon a semigroup-based framework previously introduced by the authors for estimating the PINN error. While this estimator has so far been limited to academic examples - due to the need to compute quantities related to input-to-state stability - we extend its applicability to a significantly broader class of problems. This is accomplished by modifying the error bound and proposing numerical strategies to approximate the required stability parameters. The extended framework enables the certification of PINN predictions in more realistic scenarios, as demonstrated by a numerical study of Stokes flow around a cylinder. |
| title | Prediction error certification for PINNs: Theory, computation, and application to Stokes flow |
| topic | Numerical Analysis Machine Learning 65N15, 47D06, 35A35, 35F16, 41A65 |
| url | https://arxiv.org/abs/2508.07994 |