4 citations · 7 across the 6 of their papers we have counts for
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math.NA2024★ 4 cited
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems
Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox +1
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use…
stat.ML2024★ 1 cited
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…