180 citations · 1.1k across the 26 of their papers we have counts for
26 papers
Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion
Rebekah D. White, John D. Jakeman, Tim Wildey +1
Bayesian inference provides a powerful tool for leveraging observational data to inform model predictions and uncertainties. However, when such data is limited, Bayesian inference…
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Matthew Lowery, John Turnage, Zachary Morrow +4
This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for…
Grouped approximate control variate estimators
Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred
This paper analyzes the approximate control variate (ACV) approach to multifidelity uncertainty quantification in the case where weighted estimators are combined to form the compon…
Multifidelity uncertainty quantification with models based on dissimilar parameters
Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred +3
Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the…
Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems
William Reese, Joseph Hart, Bart van Bloemen Waanders +3
Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain…
Randomized Algorithms for Scientific Computing (RASC)
Aydin Buluc, Tamara G. Kolda, Stefan M. Wild +16
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office o…