Sparse learning of stochastic dynamical equations
| dc.citation.articleNumber | 241723 | en_US |
| dc.citation.issueNumber | 24 | en_US |
| dc.citation.journalTitle | The Journal of Chemical Physics | en_US |
| dc.citation.volumeNumber | 148 | en_US |
| dc.contributor.author | Boninsegna, Lorenzo | en_US |
| dc.contributor.author | Nüske, Feliks | en_US |
| dc.contributor.author | Clementi, Cecilia | en_US |
| dc.date.accessioned | 2018-07-16T21:54:26Z | en_US |
| dc.date.available | 2018-07-16T21:54:26Z | en_US |
| dc.date.issued | 2018 | en_US |
| dc.description.abstract | With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process. | en_US |
| dc.identifier.citation | Boninsegna, Lorenzo, Nüske, Feliks and Clementi, Cecilia. "Sparse learning of stochastic dynamical equations." <i>The Journal of Chemical Physics,</i> 148, no. 24 (2018) AIP Publishing: https://doi.org/10.1063/1.5018409. | en_US |
| dc.identifier.doi | https://doi.org/10.1063/1.5018409 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/102450 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | AIP Publishing | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.title | Sparse learning of stochastic dynamical equations | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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