Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916948659404800 |
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| author | Kong, Fanze Lai, Chen-Chih Lu, Yubin |
| author_facet | Kong, Fanze Lai, Chen-Chih Lu, Yubin |
| contents | Conservative-dissipative dynamics are ubiquitous across a variety of complex open systems. We propose a data-driven two-phase method, the Moment-DeepRitz Method, for learning drift decompositions in generalized diffusion systems involving conservative-dissipative dynamics. The method is robust to noisy data, adaptable to rough potentials and oscillatory rotations. We demonstrate its effectiveness through several numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts Kong, Fanze Lai, Chen-Chih Lu, Yubin Machine Learning Computational Physics Conservative-dissipative dynamics are ubiquitous across a variety of complex open systems. We propose a data-driven two-phase method, the Moment-DeepRitz Method, for learning drift decompositions in generalized diffusion systems involving conservative-dissipative dynamics. The method is robust to noisy data, adaptable to rough potentials and oscillatory rotations. We demonstrate its effectiveness through several numerical experiments. |
| title | Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2509.10495 |