Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Cheng, Yingda"'
In this paper, we develop a low-rank method with high-order temporal accuracy using spectral deferred correction (SDC) to compute linear matrix differential equations. In [1], a low rank numerical method is proposed to correct the modeling error of t
Externí odkaz:
http://arxiv.org/abs/2412.09400
This work proposes a new class of preconditioners for the low rank Generalized Minimal Residual Method (GMRES) for multiterm matrix equations arising from implicit timestepping of linear matrix differential equations. We are interested in computing l
Externí odkaz:
http://arxiv.org/abs/2410.07465
Autor:
Appelo, Daniel, Cheng, Yingda
We design high order accurate methods that exploit low rank structure in the density matrix while respecting the essential structure of the Lindblad equation. Our methods preserves complete positivity and are trace preserving.
Externí odkaz:
http://arxiv.org/abs/2409.08898
Autor:
Appelö, Daniel, Cheng, Yingda
In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac-Frenkel time-depend
Externí odkaz:
http://arxiv.org/abs/2402.05347
Kinetic transport equations are notoriously difficult to simulate because of their complex multiscale behaviors and the need to numerically resolve a high dimensional probability density function. Past literature has focused on building reduced order
Externí odkaz:
http://arxiv.org/abs/2211.04677
This paper reviews the adaptive sparse grid discontinuous Galerkin (aSG-DG) method for computing high dimensional partial differential equations (PDEs) and its software implementation. The C\texttt{++} software package called AdaM-DG, implementing th
Externí odkaz:
http://arxiv.org/abs/2211.01531
This paper considers the discontinuous Galerkin (DG) methods for solving the Vlasov-Maxwell (VM) system, a fundamental model for collisionless magnetized plasma. The DG methods provide accurate numerical description with conservation and stability pr
Externí odkaz:
http://arxiv.org/abs/2210.07908
This is the third paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation (RTE). In our previous work \cite{huang2021gradient}, we proposed an approach to learn the gradient of the unclose
Externí odkaz:
http://arxiv.org/abs/2109.00700
This is the second paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation (RTE). In our previous work \cite{huang2021gradient}, we proposed an approach to directly learn the gradient of t
Externí odkaz:
http://arxiv.org/abs/2105.14410
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. Instead of learning the unclosed high order moment, we propose to directly learn the gradient of
Externí odkaz:
http://arxiv.org/abs/2105.05690