[2604.01231] Experimental Design for Missing Physics

Physics > Computational Physics arXiv:2604.01231 (physics) [Submitted on 21 Mar 2026] Title:Experimental Design for Missing Physics Authors:Arno Strouwen, Sebastián Micluţa-Câmpeanu View a PDF of the paper titled Experimental Design for Missing Physics, by Arno Strouwen and 1 other authors View PDF HTML (experimental) Abstract: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 discriminating between the plausible model structures suggested by symbolic regression. This technique is then applied to discovering the missing physics of a bioreactor. Subjects: Computational Physics (physics.comp-ph); Machine Learning (cs.LG) Cite as: arXiv:2604.01231 [physics.comp-ph]   (or arXiv:2604.01231v1 [physics.comp-ph] for this version)   https://doi.org/10.48550/arXiv.2604.01231 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IFAC-Paper...