Work

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

Year: 2022

Type: article

Source: Journal of Computational Physics

DOI: 10.1016/j.jcp.2022.111313

Authors: Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields

Institutions: Johns Hopkins University, Siemens (Germany), Technische Universität Darmstadt

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

Funders: U.S. Department of Energy, California Department of Fish and Game, Deutsche Forschungsgemeinschaft, Technische Universität Darmstadt, Office of Science, Advanced Scientific Computing Research

Awards: G367514008, G7001731459, G8035884955

OpenAlex ID: W4226464741

FWCI:

Cites: 137

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Related to: 10