Sparse Identification of Nonlinear Dynamics for Stochastic Delay Differential Equations
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909722122125312 |
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| author | Breda, Dimitri Conte, Dajana D'Ambrosio, Raffaele Santaniello, Ida Tanveer, Muhammad |
| author_facet | Breda, Dimitri Conte, Dajana D'Ambrosio, Raffaele Santaniello, Ida Tanveer, Muhammad |
| contents | A general framework for recovering drift and diffusion dynamics from sampled trajectories is presented for the first time for stochastic delay differential equations. The core relies on the well-established SINDy algorithm for the sparse identification of nonlinear dynamics. The proposed methodology combines recently proposed high-order estimates of drift and covariance for dealing with stochastic problems with augmented libraries to handle delayed arguments. Three different strategies are discussed in view of exploiting only realistically available data. A thorough comparative numerical investigation is performed on different models, which helps guiding the choice of effective and possibly outperforming schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Identification of Nonlinear Dynamics for Stochastic Delay Differential Equations Breda, Dimitri Conte, Dajana D'Ambrosio, Raffaele Santaniello, Ida Tanveer, Muhammad Numerical Analysis 60H10, 34K50, 68T05, 93E12 A general framework for recovering drift and diffusion dynamics from sampled trajectories is presented for the first time for stochastic delay differential equations. The core relies on the well-established SINDy algorithm for the sparse identification of nonlinear dynamics. The proposed methodology combines recently proposed high-order estimates of drift and covariance for dealing with stochastic problems with augmented libraries to handle delayed arguments. Three different strategies are discussed in view of exploiting only realistically available data. A thorough comparative numerical investigation is performed on different models, which helps guiding the choice of effective and possibly outperforming schemes. |
| title | Sparse Identification of Nonlinear Dynamics for Stochastic Delay Differential Equations |
| topic | Numerical Analysis 60H10, 34K50, 68T05, 93E12 |
| url | https://arxiv.org/abs/2508.03040 |