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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08561 |
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| _version_ | 1866918196937752576 |
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| author | Penuela, J. Ouerdane, H. |
| author_facet | Penuela, J. Ouerdane, H. |
| contents | Physics-Informed Neural Networks (PINNs) have emerged as a promising framework for solving forward and inverse problems governed by differential equations. However, their reliability when used in ill-posed inverse problems remains poorly understood. In this study, we explore the fundamental limitations of PINNs using a simple illustrative case: RC low-pass filters. Showing that while PINNs can accurately predict system dynamics in forward problems, they fail to recover unique physical parameters when solving inverse problems when more than two parameters are approximated. Our findings provide grounds to understand the boundaries of PINNs applicability for parameter discovery in physical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The curse of dimensionality: what lies beyond the capabilities of physics-informed neural networks Penuela, J. Ouerdane, H. Systems and Control Physics-Informed Neural Networks (PINNs) have emerged as a promising framework for solving forward and inverse problems governed by differential equations. However, their reliability when used in ill-posed inverse problems remains poorly understood. In this study, we explore the fundamental limitations of PINNs using a simple illustrative case: RC low-pass filters. Showing that while PINNs can accurately predict system dynamics in forward problems, they fail to recover unique physical parameters when solving inverse problems when more than two parameters are approximated. Our findings provide grounds to understand the boundaries of PINNs applicability for parameter discovery in physical systems. |
| title | The curse of dimensionality: what lies beyond the capabilities of physics-informed neural networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.08561 |