CONFIDE: Contextual Finite Differences Modelling of PDEs
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910475792416768 |
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| author | Linial, Ori Avner, Orly Di Castro, Dotan |
| author_facet | Linial, Ori Avner, Orly Di Castro, Dotan |
| contents | We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context. The training phase integrates knowledge of the form of the equation with a differential scheme, while the inference phase yields a PDE that fits the data sample and enables both signal prediction and data explanation. We include results of extensive experimentation, comparing our method to SOTA approaches, together with ablation studies that examine different flavors of our solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15827 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CONFIDE: Contextual Finite Differences Modelling of PDEs Linial, Ori Avner, Orly Di Castro, Dotan Machine Learning Numerical Analysis We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context. The training phase integrates knowledge of the form of the equation with a differential scheme, while the inference phase yields a PDE that fits the data sample and enables both signal prediction and data explanation. We include results of extensive experimentation, comparing our method to SOTA approaches, together with ablation studies that examine different flavors of our solution. |
| title | CONFIDE: Contextual Finite Differences Modelling of PDEs |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2303.15827 |