Graphical conditional generative modeling for digital twin modeling
Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing. As an alternative, one can seek parsimonious stochastic surrogate models built only on the variables needed to describe the relevant quantities of interest. We introduce a framework for discovering such variables from observational data by identifying which candidate inputs influence the full conditional law of a target quantity, rather than only its ...
| Authors | Zongren Zou, Théo Bourdais, Ricardo Baptista et al. |
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| Primary category | cs.CE |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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