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.
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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.