13 citations · 25 across the 3 of their papers we have counts for
3 papers · 1 filter
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale
Michael G. Kapteyn, Jacob V. R. Pretorius, Karen E. Willcox
A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work pro…
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning
Michael G. Kapteyn, Karen E. Willcox
This work develops a methodology for creating a data-driven digital twin from a library of physics-based models representing various asset states. The digital twin is updated using…
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
Peter Benner, Pawan Goyal, Boris Kramer +2
This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that…