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20222026
most citedEnergy diminishing implicit-explicit Runge--Kutta methods for gradient flows

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

math.NA2026

Optimal Sobolev Approximation by Deterministic and Random Shallow Sigmoidal Networks

Zhaohui Fu, Yangshuai Wang

Shallow networks with prescribed or randomly sampled hidden parameters are widely used as numerical trial spaces, yet their optimal Sobolev approximation power with standard smooth…

math.NA2026

A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge--Kutta Time Stepping

Haoran Zhou, Zhaohui Fu, Yangshuai Wang +1

We study a discrete-time random feature method for nonlinear, time-dependent partial differential equations. In contrast to continuous-time formulations that treat time as an addit…

math.NA2024

Higher-Order Energy-Decreasing Exponential Time Differencing Runge-Kutta methods for Gradient Flows

Zhaohui Fu, Jie Shen, Jiang Yang

In this paper, we develop a general framework for constructing higher-order, unconditionally energy-stable exponential time differencing Runge-Kutta methods applicable to a range o…

math.NA2022

Energy plus maximum bound preserving Runge-Kutta methods for the Allen-Cahn equation

Zhaohui Fu, Tao Tang, Jiang Yang

It is difficult to design high order numerical schemes which could preserve both the maximum bound property (MBP) and energy dissipation law for certain phase field equations. Stro…

math.NA2022★ 1 cited

Energy diminishing implicit-explicit Runge--Kutta methods for gradient flows

Zhaohui Fu, Tao Tang, Jiang Yang

This study focuses on the development and analysis of a group of high-order implicit-explicit (IMEX) Runge--Kutta (RK) methods that are suitable for discretizing gradient flows wit…