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Extreme-scale UQ for Bayesian inverse problems governed by PDEs
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Extreme-scale UQ for Bayesian inverse problems governed by PDEs (2012)Extreme-scale UQ for Bayesian inverse pro…Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network (2023)Large-Scale Bayesian Optimal …A general framework for quantifying uncertainty at scale (2022)A general framework for quant…Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (2019)Taylor approximation and vari…A GRADIENT-BASED SAMPLING APPROACH FOR DIMENSION REDUCTION OF PARTIAL DIFFERENTIAL EQUATIONS WITH STOCHASTIC COEFFICIENTS (2014)A GRADIENT-BASED SAMPLING APP…Stochastic Finite Elements: A Spectral Approach (1991)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Sparse Collocation Methods for Stochastic Interpolation and Quadrature (2017)Lectures on Stochastic Programming: Modeling and Theory (2009)Theory and Applications of Robust Optimization (2010)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (2021)A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (2023)A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (2023)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global Sensitivity Analysis. The Primer (2007)Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification (2023)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification (2014)Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet (2015)Variational Bayesian experimental design for geophysical applications: seismic source location, amplitude versus offset inversion, and estimating CO2 saturations in a subsurface reservoir (2023)
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  • 2019Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty42 cited
  • 2023Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network27 cited
  • 2022A general framework for quantifying uncertainty at scale17 cited
  • 2014A GRADIENT-BASED SAMPLING APPROACH FOR DIMENSION REDUCTION OF PARTIAL DIFFERENTIAL EQUATIONS WITH STOCHASTIC COEFFICIENTS15 cited

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