A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
Bai, Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems, Applied numerical mathematics 43 (1-2) (2002) 9–44
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Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.