Machine Learning Conservation Laws of Dynamical systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866916267576786944 |
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| author | Mebratie, Meskerem Abebaw Nather, Rüdiger von Rudorff, Guido Falk Seiler, Werner M. |
| author_facet | Mebratie, Meskerem Abebaw Nather, Rüdiger von Rudorff, Guido Falk Seiler, Werner M. |
| contents | Conservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using trajectory data. It is the first such approach based on kernel methods instead of neural networks which leads to lower computational costs and requires a lower amount of training data. We propose the use of an "indeterminate" form of kernel ridge regression where the labels still have to be found by additional conditions. We use here a simple approach minimising the length of the coefficient vector to discover a single conservation law. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Conservation Laws of Dynamical systems Mebratie, Meskerem Abebaw Nather, Rüdiger von Rudorff, Guido Falk Seiler, Werner M. Computational Physics Conservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using trajectory data. It is the first such approach based on kernel methods instead of neural networks which leads to lower computational costs and requires a lower amount of training data. We propose the use of an "indeterminate" form of kernel ridge regression where the labels still have to be found by additional conditions. We use here a simple approach minimising the length of the coefficient vector to discover a single conservation law. |
| title | Machine Learning Conservation Laws of Dynamical systems |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2405.20857 |