Data-dependent density estimation for the Fokker-Planck equation in higher dimensions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916105931456512 |
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| author | Jensen, Max Merle, Fabian Prohl, Andreas |
| author_facet | Jensen, Max Merle, Fabian Prohl, Andreas |
| contents | We present a new strategy to approximate the global solution of the Fokker-Planck equation efficiently in higher dimensions and show its convergence. The main ingredients are the Euler scheme to solve the associated stochastic differential equation and a histogram method for tree-structured density estimation on a data-dependent partitioning of the state space R^d. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-dependent density estimation for the Fokker-Planck equation in higher dimensions Jensen, Max Merle, Fabian Prohl, Andreas Numerical Analysis 65M75, 65M12, 65C30 We present a new strategy to approximate the global solution of the Fokker-Planck equation efficiently in higher dimensions and show its convergence. The main ingredients are the Euler scheme to solve the associated stochastic differential equation and a histogram method for tree-structured density estimation on a data-dependent partitioning of the state space R^d. |
| title | Data-dependent density estimation for the Fokker-Planck equation in higher dimensions |
| topic | Numerical Analysis 65M75, 65M12, 65C30 |
| url | https://arxiv.org/abs/2401.14685 |