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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.20869 |
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| _version_ | 1866914171305590784 |
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| author | Crislip, Eric Khalil, Mohammad Portone, Teresa Chkrebtii, Oksana Neal, Kyle |
| author_facet | Crislip, Eric Khalil, Mohammad Portone, Teresa Chkrebtii, Oksana Neal, Kyle |
| contents | Closure modeling - the statistical modeling of missing dynamics in the natural sciences and engineering - is a growing and active area of research. Existing methods for closure modeling are often computationally prohibitive, lack uncertainty quantification, or require noise-free observations of the temporal derivatives over the system state. We propose a novel, computationally efficient approach for the modeling and estimation of closure terms over the spatiotemporal domain that provides uncertainty quantification and is effective even when the observations of the system state are sparse or contain moderate levels of noise. The efficacy of our approach is demonstrated in both one and two spatial dimensions through numerical experiments using the Fisher-KPP reaction-diffusion equation and the advection-diffusion equation as exemplars. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Closure Term Estimation in Spatiotemporal Models of Dynamical Systems Crislip, Eric Khalil, Mohammad Portone, Teresa Chkrebtii, Oksana Neal, Kyle Methodology Closure modeling - the statistical modeling of missing dynamics in the natural sciences and engineering - is a growing and active area of research. Existing methods for closure modeling are often computationally prohibitive, lack uncertainty quantification, or require noise-free observations of the temporal derivatives over the system state. We propose a novel, computationally efficient approach for the modeling and estimation of closure terms over the spatiotemporal domain that provides uncertainty quantification and is effective even when the observations of the system state are sparse or contain moderate levels of noise. The efficacy of our approach is demonstrated in both one and two spatial dimensions through numerical experiments using the Fisher-KPP reaction-diffusion equation and the advection-diffusion equation as exemplars. |
| title | Closure Term Estimation in Spatiotemporal Models of Dynamical Systems |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.20869 |