8 citations · 19 across the 10 of their papers we have counts for
7 papers · 1 filter
Second-order consistency for learning chaotic dynamics via randomized Jacobian matching
Shinhoo Kang, Hai V. Nguyen, Tan Bui-Thanh
Short-horizon accuracy does not ensure that a learned chaotic system has correct long-time dynamics. Trajectory (zeroth-order) matching constrains vector-field values, and Jacobian…
Unified -HDG Frameworks for Friedrichs' PDE systems
Jau-Uei Chen, Shinhoo Kang, Tan Bui-Thanh +1
This work proposes a unified -adaptivity framework for hybridized discontinuous Galerkin (HDG) method for a large class of partial differential equations (PDEs) of Friedrichs'…
Learning Subgrid-scale Models with Neural Ordinary Differential Equations
Shinhoo Kang, Emil M. Constantinescu
We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaoti…
Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes Equations
Shinhoo Kang, Alp Dener, Aidan Hamilton +3
Earth system models are complex integrated models of atmosphere, ocean, sea ice, and land surface. Coupling the components can be a significant challenge due to the difference in p…
Entropy-Preserving and Entropy-Stable Relaxation IMEX and Multirate Time-Stepping Methods
Shinhoo Kang, Emil M. Constantinescu
We propose entropy-preserving and entropy-stable partitioned Runge--Kutta (RK) methods. In particular, we extend the explicit relaxation Runge--Kutta methods to IMEX--RK methods an…
Mass-Conserving Implicit-Explicit Methods for Coupled Compressible Navier-Stokes Equations
Shinhoo Kang, Emil M. Constantinescu, Hong Zhang +1
Earth system models are composed of coupled components that separately model systems such as the global atmosphere, ocean, and land surface. While these components are well develop…
A scalable exponential-DG approach for nonlinear conservation laws: with application to Burger and Euler equations
Shinhoo Kang, Tan Bui-Thanh
We propose an Exponential DG approach for numerically solving partial differential equations (PDEs). The idea is to decompose the governing PDE operators into linear (fast dynamics…