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Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network

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2012Simulation-based optimal Bayesian experimental design for nonlinear systems466 cited
2015Scalable 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 sheet155 cited
2014A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification136 cited
2016A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems131 cited
2013Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations129 cited
2014GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN113 cited
2018Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain78 cited
2015On Bayesian A- and D-Optimal Experimental Designs in Infinite Dimensions76 cited
2018Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems59 cited
2012Extreme-scale UQ for Bayesian inverse problems governed by PDEs56 cited
2015Fast Bayesian optimal experimental design for seismic source inversion46 cited

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2023A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty6 cited