Experimental Design for Missing Physics
For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a popular tool to discover these missing physics. Universal differential equations employ neural networks to represent missing parts of the model structure, and symbolic regression aims to make these neural networks interpretable. These machine learning techniques require high-quality data to successfully recover the true model structure. To gather such informative data, a sequential experimental design technique is developed which is based on optimally disc...
| Authors | Arno Strouwen, Sebastián Micluţa-Câmpeanu |
|---|---|
| Primary category | stat.ML |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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