Generalized Forcing Method: Generation of diverse data for training linear transport PDE closure models

This archive contains the reproduction data, reduced models, plotting scripts, and precomputed figures for the paper “Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models.” The archive includes five model-evaluation cases: two homogeneous parallel-flow cases, two inhomogeneous-flow cases, and one homogeneous random-flow case. The provided scripts evaluate the reduced models against the included test solutions and reproduce the RMSE convergence plots and eGFM dataset-summary plots reported in the manuscript. The release is intended for figure reproduction and model-evaluation verification. It does not include the full simulation code or the model-fitting code.

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Publication Details

Journal
Physical Review Fluids
Published
2026-10-13
DOI
https://doi.org/10.1103/52jk-xb5y
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Generalized Forcing Method: Generation of diverse data for training linear transport PDE closure models

Wenyuan Xue, Ali Mani
Physical Review Fluids
Model Reduction and Neural Networks
article

Generalized Forcing Method: Generation of diverse data for training linear transport PDE closure models

Wenyuan Xue, Ali Mani
article en

Abstract

This archive contains the reproduction data, reduced models, plotting scripts, and precomputed figures for the paper “Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models.” The archive includes five model-evaluation cases: two homogeneous parallel-flow cases, two inhomogeneous-flow cases, and one homogeneous random-flow case. The provided scripts evaluate the reduced models against the included test solutions and reproduce the RMSE convergence plots and eGFM dataset-summary plots reported in the manuscript. The release is intended for figure reproduction and model-evaluation verification. It does not include the full simulation code or the model-fitting code.

Physical Review Fluids
Office of Naval Research
Openalex Percentile: Top 56%
Model Reduction and Neural Networks
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