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03/20/2023
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
In complex large-scale systems such as climate, important effects are ca...
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12/23/2022
Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems
Inverse problems constrained by partial differential equations (PDEs) pl...
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10/17/2022
Hyper-differential sensitivity analysis with respect to model discrepancy: Calibration and optimal solution updating
Optimization constrained by computational models is common across scienc...
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10/17/2022
Hyper-differential sensitivity analysis with respect to model discrepancy: Mathematics and computation
Model discrepancy, defined as the difference between model predictions a...
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09/23/2022
A new perspective on parameter study of optimization problems
We provide a new perspective on the study of parameterized optimization ...
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02/04/2022
Hyper-differential sensitivity analysis for nonlinear Bayesian inverse problems
We consider hyper-differential sensitivity analysis (HDSA) of nonlinear ...
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06/22/2021
Enabling and interpreting hyper-differential sensitivity analysis for Bayesian inverse problems
Inverse problems constrained by partial differential equations (PDEs) pl...
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07/26/2020
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
Deep neural networks (DNNs) have achieved state-of-the-art performance a...
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02/07/2020
Randomized Algorithms for Generalized Singular Value Decomposition with Application to Sensitivity Analysis
The generalized singular value decomposition (GSVD) is a valuable tool t...
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12/17/2018
Robustness of the Sobol' indices to marginal distribution uncertainty
Global sensitivity analysis (GSA) quantifies the influence of uncertain ...
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03/29/2018
Robustness of the Sobol' indices to distributional uncertainty
Global sensitivity analysis (GSA) is used to quantify the influence of u...
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08/24/2017