no code implementations • 9 Jan 2024 • Andrew J. Christlieb, Mingchang Ding, Juntao Huang, Nicholas A. Krupansky
This closure is motivated by the exact closure for the free streaming limit that we derived in our paper on closures in transport \cite{Huang2022-RTE1}.
no code implementations • 2 Sep 2021 • Juntao Huang, Yingda Cheng, Andrew J. Christlieb, Luke F. Roberts
In our second paper \cite{huang2021hyperbolic}, we identified a symmetrizer which leads to conditions that enforce that the gradient based ML closure is symmetrizable hyperbolic and stable over long time.
no code implementations • 30 May 2021 • Juntao Huang, Yingda Cheng, Andrew J. Christlieb, Luke F. Roberts, Wen-An Yong
This is the second paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation (RTE).
no code implementations • 12 May 2021 • Juntao Huang, Yingda Cheng, Andrew J. Christlieb, Luke F. Roberts
In this paper, we take a data-driven approach and apply machine learning to the moment closure problem for radiative transfer equation in slab geometry.
1 code implementation • 17 Jan 2021 • Juntao Huang, Yizhou Zhou, Wen-An Yong
First, we use a single matrix to represent the stoichiometric coefficients for both the reactants and products in a system without catalysis reactions.
no code implementations • 28 Sep 2020 • Juntao Huang, Zhiting Ma, Yizhou Zhou, Wen-An Yong
In this work, we develop a method for learning interpretable, thermodynamically stable and Galilean invariant partial differential equations (PDEs) based on the Conservation-dissipation Formalism of irreversible thermodynamics.
1 code implementation • 3 Jun 2019 • Juntao Huang, Yingda Cheng
Theoretical and numerical studies are performed taking into consideration of accuracy and stability with regard to the choice of the interpolatory multiwavelets.
Numerical Analysis Numerical Analysis