A derivative-based upper bound on iEIG combined with likelihood-informed subspace projectors and conditional measure transport maps yields a scalable unified framework for sOED and amortized inference in high- and infinite-dimensional PDE inverse problems.
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Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging
A derivative-based upper bound on iEIG combined with likelihood-informed subspace projectors and conditional measure transport maps yields a scalable unified framework for sOED and amortized inference in high- and infinite-dimensional PDE inverse problems.