Work

Discovering and forecasting extreme events via active learning in neural operators

Year: 2022

Type: article

Source: Nature Computational Science

DOI: 10.1038/s43588-022-00376-0

Authors: Ethan Pickering, Stephen Guth, George Em Karniadakis, Themistoklis P. Sapsis

Institutions: Massachusetts Institute of Technology, Brown University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: closed

Funders: U.S. Department of Defense, Defense Advanced Research Projects Agency, Multidisciplinary University Research Initiative, Office of Naval Research, Air Force Office of Scientific Research

Awards: G1523888516, G2549266966, PHYSICS-INFORMED, MACHINE LEARNING METHODS FOR THE QUANTIFICATION OF EXTREME OCEAN EVENTS FOR NAVAL VESSELS, G4014538961, G4516736450, G4640328899, BAYESIAN EXPERIMENTAL DESIGN WITH ACTIVE LEARNING ALGORITHMS, G5550151705, G561362341, G6098521345, G6670623497, G8876996369

OpenAlex ID: W4313430604

FWCI: 5.51

Cites: 39

Cited by: 74

Related to: 10