Designing Poisson Integrators Through Machine Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929296101081088 |
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| author | Vaquero, Miguel de Diego, David Martín Cortés, Jorge |
| author_facet | Vaquero, Miguel de Diego, David Martín Cortés, Jorge |
| contents | This paper presents a general method to construct Poisson integrators, i.e., integrators that preserve the underlying Poisson geometry. We assume the Poisson manifold is integrable, meaning there is a known local symplectic groupoid for which the Poisson manifold serves as the set of units. Our constructions build upon the correspondence between Poisson diffeomorphisms and Lagrangian bisections, which allows us to reformulate the design of Poisson integrators as solutions to a certain PDE (Hamilton-Jacobi). The main novelty of this work is to understand the Hamilton-Jacobi PDE as an optimization problem, whose solution can be easily approximated using machine learning related techniques. This research direction aligns with the current trend in the PDE and machine learning communities, as initiated by Physics- Informed Neural Networks, advocating for designs that combine both physical modeling (the Hamilton-Jacobi PDE) and data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_20139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing Poisson Integrators Through Machine Learning Vaquero, Miguel de Diego, David Martín Cortés, Jorge Mathematical Physics Machine Learning Numerical Analysis Differential Geometry Dynamical Systems 37J06, 70H15, 70H20, 70G45, 65L05, 68T07 G.1.8; J.2 This paper presents a general method to construct Poisson integrators, i.e., integrators that preserve the underlying Poisson geometry. We assume the Poisson manifold is integrable, meaning there is a known local symplectic groupoid for which the Poisson manifold serves as the set of units. Our constructions build upon the correspondence between Poisson diffeomorphisms and Lagrangian bisections, which allows us to reformulate the design of Poisson integrators as solutions to a certain PDE (Hamilton-Jacobi). The main novelty of this work is to understand the Hamilton-Jacobi PDE as an optimization problem, whose solution can be easily approximated using machine learning related techniques. This research direction aligns with the current trend in the PDE and machine learning communities, as initiated by Physics- Informed Neural Networks, advocating for designs that combine both physical modeling (the Hamilton-Jacobi PDE) and data. |
| title | Designing Poisson Integrators Through Machine Learning |
| topic | Mathematical Physics Machine Learning Numerical Analysis Differential Geometry Dynamical Systems 37J06, 70H15, 70H20, 70G45, 65L05, 68T07 G.1.8; J.2 |
| url | https://arxiv.org/abs/2403.20139 |