A grouped pooling strategy with ensemble Kalman inversion improves accuracy of expected information gain estimators in Bayesian experimental design at amortized computational cost.
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A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
Presents a scalable ROED framework for PDE-constrained nonlinear Bayesian inverse problems with EIG approximations, eigenvalue sensitivity gradients, and probabilistic max-min optimization, illustrated on elliptic PDE sensor placement.
citing papers explorer
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Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
A grouped pooling strategy with ensemble Kalman inversion improves accuracy of expected information gain estimators in Bayesian experimental design at amortized computational cost.
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Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
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Robust optimal design of large-scale Bayesian nonlinear inverse problems
Presents a scalable ROED framework for PDE-constrained nonlinear Bayesian inverse problems with EIG approximations, eigenvalue sensitivity gradients, and probabilistic max-min optimization, illustrated on elliptic PDE sensor placement.