An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling

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 unavail...

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Bibliographic Details
Published in:	Nonlinear processes in geophysics Vol. 30; no. 3; pp. 263 - 276
Main Authors:	Shi, Bin, Sun, Guodong
Format:	Journal Article
Language:	English
Published:	Gottingen Copernicus GmbH 06.07.2023 Copernicus Publications
Subjects:	Algorithms Analysis Atmosphere Atmospheric models Burgers equation Computation Computing time Fluid dynamics General circulation models Machine learning Mathematical models Methods Modelling Numerical models Objective function Ocean models Optimization Ordinary differential equations Perturbation Perturbations Probability theory Sampling Viscosity
ISSN:	1607-7946, 1023-5809, 1607-7946
Online Access:	Get full text
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