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.