On the use of recurrent neural networks for fast and accurate non-uniform gas radiation modeling-Reference-Cited by-同舟云学术

On the use of recurrent neural networks for fast and accurate non-uniform gas radiation modeling

Published:2022-12 Issue: Volume:293 Page:108371
ISSN:0022-4073
Container-title:Journal of Quantitative Spectroscopy and Radiative Transfer
language:en
Short-container-title:Journal of Quantitative Spectroscopy and Radiative Transfer

Author:

André Frédéric,Cornet Céline,Delage Cindy,Dubuisson Philippe,Galtier Mathieu

Funder

Agence Nationale de la Recherche

Publisher

Elsevier BV

Subject

Spectroscopy,Atomic and Molecular Physics, and Optics,Radiation

Reference40 articles.

1. A new method based on artificial neural network for radiative heat transfer calculation: comparison with benchmark numerical solutions in homogeneous media;Royer,2020

2. Physics informed neural networks for simulating radiative transfer;Mishra;JQSRT,2021

3. A machine learning based efficient and compact full-spectrum correlated k-distribution model;Zhou;JQSRT,2020

4. A deep learning approach to fast radiative transfer;Stegmann;JQSRT,2022

5. Radiative transfer in the O2 a-band – a fast and accurate forward model based on the L-distribution approach;Andre;JQSRT,2021

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1. Bridging physics and statistical learning methodologies for the accurate modeling of the radiative properties of non-uniform atmospheric paths;Journal of Quantitative Spectroscopy and Radiative Transfer;2024-07

2. BRIDGING PHYSICS AND STATISTICAL LEARNING METHODOLOGIES FOR THE ACCURATE MODELING OF THE RADIATIVE PROPERTIES OF NON-UNIFORM ATMOSPHERIC PATHS;Proceeding of Proceedings of the 10th International Symposium on Radiative Transfer, RAD-23 Thessaloniki, Greece, 12–16 June 2023;2023

3. A QUASI MONTE CARLO SOLVER FOR GAS RADIATION USING ℓ-DISTRIBUTION MODEL;Proceeding of Proceedings of the 10th International Symposium on Radiative Transfer, RAD-23 Thessaloniki, Greece, 12–16 June 2023;2023