293 citations · 1.2k across the 39 of their papers we have counts for
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A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations
Shengrong Ding, Kailiang Wu
This paper proposes and analyzes a novel efficient high-order finite volume method for the ideal magnetohydrodynamics (MHD). As a distinctive feature, the method simultaneously pre…
Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics
Chaoyi Cai, Jianxian Qiu, Kailiang Wu
The relativistic hydrodynamics (RHD) equations have three crucial intrinsic physical constraints on the primitive variables: positivity of pressure and density, and subluminal flui…
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Ce Zhang, Kailiang Wu, Zhihai He
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavio…
Deep-OSG: Deep Learning of Operators in Semigroup
Junfeng Chen, Kailiang Wu
This paper proposes a novel deep learning approach for learning operators in semigroup, with applications to modeling unknown autonomous dynamical systems using time series data co…