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An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling
March 26, 2024, 4:44 a.m. | Bin Shi, Guodong Sun
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learning techniques to obtain conditional nonlinear optimal perturbations (CNOPs), which is different from traditional (deterministic) optimization methods.1 Specifically, the traditional approach is unavailable in practice, which requires numerically computing the gradient (first-order information) such that the computation cost is expensive, since it needs a large number of times to run numerical models. However, the sampling approach directly reduces the gradient to the …
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