Graphical conditional generative modeling for digital twin modeling

arXiv:2606. 16219v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

• reinforcement-learning
• efficiency
• safety