A COMPARISON OF MACHINE / STATISTICAL LEARNING STRATEGIES FOR MODELING THE RADIATIVE PROPERTIES OF NON-UNIFORM GASEOUS ATMOSPHERES

Authors

Frédéric André (ORCID: https://orcid.org/0000-0001-9657-8701)
L. Guilmard
Tao Ren (ORCID: https://orcid.org/0000-0002-8862-797X)
X. Aihemaiti

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1615/ichmt.2025.____conf_suffix__.30
Primary Topic: Meteorological Phenomena and Simulations
Type: article
Field-Weighted Citation Impact: 0.00

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article

A COMPARISON OF MACHINE / STATISTICAL LEARNING STRATEGIES FOR MODELING THE RADIATIVE PROPERTIES OF NON-UNIFORM GASEOUS ATMOSPHERES

Frédéric André, L. Guilmard, Tao Ren, X. Aihemaiti

Jan 1, 2025

Meteorological Phenomena and Simulations

article

A COMPARISON OF MACHINE / STATISTICAL LEARNING STRATEGIES FOR MODELING THE RADIATIVE PROPERTIES OF NON-UNIFORM GASEOUS ATMOSPHERES

Frédéric André, L. Guilmard, Tao Ren, X. Aihemaiti

January 1, 2025article en

Abstract

No abstract available for this paper.

Openalex Percentile: Top 70%

Meteorological Phenomena and Simulations

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A COMPARISON OF MACHINE / STATISTICAL LEARNING STRATEGIES FOR MODELING THE RADIATIVE PROPERTIES OF NON-UNIFORM GASEOUS ATMOSPHERES — Frédéric André, L. Guilmard, et al. · (2025) | TGRS Research Map | TGRS