AI RESEARCH
Experimental Design for Missing Physics
arXiv CS.LG
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ArXi:2604.01231v1 Announce Type: cross 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.