Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data
A novel framework is proposed that utilizes symbolic regression via genetic programming to identify free-form partial differential equations from scarce and noisy data. The framework successfully identified ground truth models for four synthetic systems (an isothermal plug flow reactor, a continuous...
| Veröffentlicht in: | Journal of computational physics. - 1986. - 514(2024) vom: 01. Sept. |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Journal of computational physics |
| Schlagworte: | Journal Article Genetic Programming Model Discovery Partial Differential Equations Symbolic Regression |
| Online verfügbar |
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