3 papers
math.NA2026
Multifidelity Proper Orthogonal Decomposition
Nicole Aretz, Karen Willcox
This paper introduces a multifidelity formulation that reduces the computational cost of the proper orthogonal decomposition (POD) of a high-fidelity model by leveraging data from…
cs.CE2025
Optimal Experimental Design of a Moving Sensor for Linear Bayesian Inverse Problems
Nicole Aretz, Thomas Lynn, Karen Willcox +1
We optimize the path of a mobile sensor to minimize the posterior uncertainty of a Bayesian inverse problem. Along its path, the sensor continuously takes measurements of the state…
cs.LG2025
Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models
Nicole Aretz, Karen Willcox
This paper presents a data-driven, nested Operator Inference (OpInf) approach for learning physics-informed reduced-order models (ROMs) from snapshot data of high-dimensional dynam…