Work

Modeling global surface dust deposition using physics-informed neural networks

Year: 2024

Type: article

Abstract: Physics-Informed Neural Networks trained with natural dust values and paleoclimatic measurements can reconstruct the global dust deposition during the Holocene and Last Glacial Maximum, complementing traditional kriging reconstruction methods.

Source: Communications Earth & Environment

DOI: 10.1038/s43247-024-01942-2

Authors: Constanza A. Molina Catricheo, Fabrice Lambert, Julien Salomon, Elwin van ’t Wout

Institutions: Pontificia Universidad Católica de Chile, University of Münster, Center for Climate and Resilience Research, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Sorbonne Université, Laboratoire Jacques-Louis Lions

Language: English

Topic: Geology and Paleoclimatology Research

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,290

Funder: Agence Nationale de la Recherche

Award: Deep Learning meets Numerical Analysis

OpenAlex ID: W4405654552

FWCI: 1.85

Cites: 53

Cited by: 10

Related to: 10