Search Results for author: Juntao Huang

Found 7 papers, 2 papers with code

Hyperbolic Machine Learning Moment Closures for the BGK Equations

no code implementations9 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}.

Machine learning moment closure models for the radiative transfer equation III: enforcing hyperbolicity and physical characteristic speeds

no code implementations2 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.

RTE

Machine learning moment closure models for the radiative transfer equation II: enforcing global hyperbolicity in gradient based closures

no code implementations30 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).

RTE

Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure

no code implementations12 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.

BIG-bench Machine Learning

Data-driven discovery of multiscale chemical reactions governed by the law of mass action

1 code implementation17 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.

Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows

no code implementations28 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.

An adaptive multiresolution discontinuous Galerkin method with artificial viscosity for scalar hyperbolic conservation laws in multidimensions

1 code implementation3 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

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