A high order IMEX method for generalized Korteweg de-Vries equations

In this paper, we introduce a high order space–time approximation of generalized Korteweg de-Vries equations. More specifically, the method uses continuous H 1 -conforming finite elements for the spatial approximation and implicit-explicit methods for the temporal approximation. The discretization implies the method is asymptotic preserving. The low-order variant is provably stable and mass conservative; the high-order variant is at least second-order accurate in both space and time, well-defined, and mass-conservative. The scheme is formulated, its properties are proven, and numerical simulations are provided to illustrate the proposed methodology.

Authors

Institutions

Publication Details

Journal
Journal of Computational and Applied Mathematics
Published
2025-08-27
DOI
https://doi.org/10.1016/j.cam.2025.116991
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A high order IMEX method for generalized Korteweg de-Vries equations

Seth Gerberding
Journal of Computational and Applied Mathematics
Advanced Numerical Methods in Computational Mathematics
article

A high order IMEX method for generalized Korteweg de-Vries equations

Seth Gerberding
article en

Abstract

In this paper, we introduce a high order space–time approximation of generalized Korteweg de-Vries equations. More specifically, the method uses continuous H 1 -conforming finite elements for the spatial approximation and implicit-explicit methods for the temporal approximation. The discretization implies the method is asymptotic preserving. The low-order variant is provably stable and mass conservative; the high-order variant is at least second-order accurate in both space and time, well-defined, and mass-conservative. The scheme is formulated, its properties are proven, and numerical simulations are provided to illustrate the proposed methodology.

Journal of Computational and Applied MathematicsVol. 475
Texas A&M University (US)
National Science Foundation, U.S. Air Force, Air Force Office of Scientific Research, Army Research Office
Openalex Percentile: Top 76%
Advanced Numerical Methods in Computational Mathematics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.