4 citations · 7 across the 6 of their papers we have counts for
3 papers · 1 filter
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri
This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation o…
Projection-based multifidelity linear regression for data-scarce applications
Vignesh Sella, Julie Pham, Karen Willcox +1
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifide…
Multifidelity linear regression for scientific machine learning from scarce data
Elizabeth Qian, Dayoung Kang, Vignesh Sella +1
Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate…