Learning closure relations using differentiable programming: An example in radiation transport-Reference-Cited by-同舟云学术

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Learning closure relations using differentiable programming: An example in radiation transport

Published:2024-05 Issue: Volume:318 Page:108941
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:

Crilly A.J.^ORCID,Duhig B.,Bouziani N.

Publisher

Elsevier BV

Reference28 articles.

1. Evaluating derivatives: principles and techniques of algorithmic differentiation;Griewank,2008

2. An analytical benchmark for non-equilibrium radiative transfer in an isotropically scattering medium;Su;Ann Nucl Energy,1997

3. Diffusion, P1, and other approximate forms of radiation transport;Olson;J Quant Spectrosc Radiat Transfer,2000

4. Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure;Huang;J Comput Phys,2022

5. Machine learning moment closure models for the radiative transfer equation II: Enforcing global hyperbolicity in gradient-based closures;Huang;Multiscale Model Simul,2023

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1. Enhancing Computational Efficiency in Porous Media Analysis: Integrating Machine Learning With Monte Carlo Ray Tracing;Journal of Thermal Science and Engineering Applications;2024-08-02

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