151 citations · 151 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems
Tiangang Cui, Youssef M. Marzouk, Karen E. Willcox
One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numeri…