Filtering Dynamical Systems Using Observations of Statistics
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| Publicado en: | arXiv.org (Feb 25, 2024), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2849177696 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1063/5.0171827 |2 doi | |
| 035 | |a 2849177696 | ||
| 045 | 0 | |b d20240225 | |
| 100 | 1 | |a Bach, Eviatar | |
| 245 | 1 | |a Filtering Dynamical Systems Using Observations of Statistics | |
| 260 | |b Cornell University Library, arXiv.org |c Feb 25, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a We consider the problem of filtering dynamical systems, possibly stochastic, using observations of statistics. Thus, the computational task is to estimate a time-evolving density \(\rho(v, t)\) given noisy observations of the true density \(\rho^\dagger\); this contrasts with the standard filtering problem based on observations of the state \(v\). The task is naturally formulated as an infinite-dimensional filtering problem in the space of densities \(\rho\). However, for the purposes of tractability, we seek algorithms in state space; specifically, we introduce a mean-field state-space model, and using interacting particle system approximations to this model, we propose an ensemble method. We refer to the resulting methodology as the ensemble Fokker-Planck filter (EnFPF). Under certain restrictive assumptions, we show that the EnFPF approximates the Kalman-Bucy filter for the Fokker-Planck equation, which is the exact solution to the infinite-dimensional filtering problem. Furthermore, our numerical experiments show that the methodology is useful beyond this restrictive setting. Specifically, the experiments show that the EnFPF is able to correct ensemble statistics, to accelerate convergence to the invariant density for autonomous systems, and to accelerate convergence to time-dependent invariant densities for non-autonomous systems. We discuss possible applications of the EnFPF to climate ensembles and to turbulence modeling. | |
| 653 | |a Exact solutions | ||
| 653 | |a Algorithms | ||
| 653 | |a Time dependence | ||
| 653 | |a Fokker-Planck equation | ||
| 653 | |a Convergence | ||
| 653 | |a Invariants | ||
| 653 | |a Dynamical systems | ||
| 653 | |a Density | ||
| 653 | |a Filtration | ||
| 653 | |a Statistics | ||
| 653 | |a State space models | ||
| 700 | 1 | |a Colonius, Tim | |
| 700 | 1 | |a Scherl, Isabel | |
| 700 | 1 | |a Stuart, Andrew | |
| 773 | 0 | |t arXiv.org |g (Feb 25, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2849177696/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2308.05484 |