This paper proposes and analyzes a novel efficient high-order finite vol...
The relativistic hydrodynamics (RHD) equations have three crucial intrin...
Given an unknown dynamical system, what is the minimum number of samples...
This paper proposes a novel deep learning approach for approximating
evo...
This paper presents the first systematic study on the fundamental proble...
We develop a new second-order unstaggered path-conservative central-upwi...
In this paper, a high-order semi-implicit (SI) asymptotic preserving (AP...
This paper designs and analyzes positivity-preserving well-balanced (WB)...
This paper presents a highly robust third-order accurate finite volume
w...
Since proposed in [X. Zhang and C.-W. Shu, J. Comput. Phys., 229: 3091–3...
In the numerical simulation of ideal MHD, keeping the pressure and densi...
This paper presents a systematic theoretical framework to derive the ene...
Solutions to many partial differential equations satisfy certain bounds ...
We present a numerical framework for deep neural network (DNN) modeling ...
This paper explores Tadmor's minimum entropy principle for the relativis...
This paper presents a class of novel high-order accurate discontinuous
G...
We study the problem of identifying unknown processes embedded in
time-d...
A novel correction algorithm is proposed for multi-class classification
...
We propose and analyze a class of robust, uniformly high-order accurate
...
We present a framework for recovering/approximating unknown time-depende...
This paper presents entropy symmetrization and high-order accurate entro...
We present a numerical approach for approximating unknown Hamiltonian sy...
We present a numerical framework for approximating unknown governing
equ...
We present effective numerical algorithms for locally recovering unknown...
We present an explicit construction for feedforward neural network (FNN)...