Uncertainty Quantification

Interpretable System Identification
Learning sparse, physically structured, and uncertainty-aware dynamics in latent spaces.
Interpretable System Identification
VENI, VINDy, VICI
VENI, VINDy, VICI is an interpretable, data-driven framework for building generative reduced-order models with uncertainty quantification, combining variational autoencoders with a probabilistic extension of SINDy.
Jonas Kneifl
Last updated on Oct 6, 2026 7 min read Model Order Reduction, Scientific Machine Learning
Project
VENI, VINDy, VICI