| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Potsdam Inst Climate Impact Res, Potsdam - Germany
[2] Free Univ Berlin, Dept Math & Comp Sci, Berlin - Germany
[3] Humboldt Univ, Phys Dept, Berlin - Germany
[4] Univ Exeter, Global Syst Inst, Exeter, Devon - England
[5] Univ Exeter, Dept Math, Exeter, Devon - England
[6] Lobachevsky State Univ Nizhni Novgorod, Nizhnii Novgorod - Russia
Total Affiliations: 6
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| Document type: | Journal article |
| Source: | NEW JOURNAL OF PHYSICS; v. 23, n. 4 APR 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Video Abstract Video Abstract: Neural partial differential equations for chaotic systems When predicting complex systems one typically relies on differential equation which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. We show that this can be used to predict paradigmatic, high-dimensional chaotic partial differential equations even when only short and incomplete datasets are available. The forecast horizon for these high dimensional systems is about an order of magnitude larger than the length of the training data. (AU) | |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |