Work

Deep transfer operator learning for partial differential equations under conditional shift

Year: 2022

Type: article

Source: Nature Machine Intelligence

DOI: 10.1038/s42256-022-00569-2

Authors: Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

Institutions: Brown University, Johns Hopkins University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Multidisciplinary University Research Initiative, Advanced Scientific Computing Research, Air Force Office of Scientific Research

Awards: G1262598121, G1523888516, PHILMS: COLLABORATORY ON MATHEMATICS AND PHYSICS INFORMED LEARNING MACHINES FOR MULTISCALE AND MULTIPHYSICS PROBLEMS, G2072716033, G36446433, G367514008, LOW-DIMENSIONAL MANIFOLD LEARNING FOR UNCERTAINTY QUANTIFICATION IN COMPLEX MULTI-SCALE STOCHASTIC SYSTEMS, LOW-DIMENSIONAL MANIFOLD LEARNING FOR UNCERTAINTY QUANTIFICATION IN COMPLEX MULTI-SCALE STOCHASTIC SYSTEMS, G5657759450, G5729145195, G6735746674, G8224071431, PHILMS: COLLABORATORY ON MATHEMATICS AND PHYSICS INFORMED LEARNING MACHINES FOR MULTISCALE AND MULTIPHYSICS PROBLEMS

OpenAlex ID: W4310497802

FWCI: 8.676

Cites: 30

Cited by: 132

Related to: 10