Kailiang Wu

Orcid: 0000-0002-9042-3909

According to our database1, Kailiang Wu authored at least 44 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

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Bibliography

2024
A New Discretely Divergence-Free Positivity-Preserving High-Order Finite Volume Method for Ideal MHD Equations.
SIAM J. Sci. Comput., February, 2024

High-Order Accurate Entropy Stable Schemes for Relativistic Hydrodynamics with General Synge-Type Equation of State.
J. Sci. Comput., February, 2024

Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics.
J. Comput. Phys., February, 2024

On Optimal Cell Average Decomposition for High-Order Bound-Preserving Schemes of Hyperbolic Conservation Laws.
SIAM J. Numer. Anal., 2024

Bound-Preserving Framework for Central-Upwind Schemes for General Hyperbolic Conservation Laws.
CoRR, 2024

High-order accurate positivity-preserving and well-balanced discontinuous Galerkin schemes for ten-moment Gaussian closure equations with source terms.
CoRR, 2024

GQL-Based Bound-Preserving and Locally Divergence-Free Central Discontinuous Galerkin Schemes for Relativistic Magnetohydrodynamics.
CoRR, 2024

2023
Geometric Quasilinearization Framework for Analysis and Design of Bound-Preserving Schemes.
SIAM Rev., November, 2023

On high order positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation.
J. Comput. Phys., November, 2023

Deep-OSG: Deep learning of operators in semigroup.
J. Comput. Phys., November, 2023

High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers.
J. Comput. Phys., September, 2023

Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?
J. Comput. Phys., March, 2023

Provably Positive Central Discontinuous Galerkin Schemes via Geometric Quasilinearization for Ideal MHD Equations.
SIAM J. Numer. Anal., February, 2023

OEDG: Oscillation-eliminating discontinuous Galerkin method for hyperbolic conservation laws.
CoRR, 2023

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems.
CoRR, 2023

Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems.
CoRR, 2023

2022
On Energy Laws and Stability of Runge-Kutta Methods for Linear Seminegative Problems.
SIAM J. Numer. Anal., 2022

Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields.
J. Comput. Phys., 2022

A physical-constraint-preserving finite volume WENO method for special relativistic hydrodynamics on unstructured meshes.
J. Comput. Phys., 2022

Deep neural network modeling of unknown partial differential equations in nodal space.
J. Comput. Phys., 2022

A New Locally Divergence-Free Path-Conservative Central-Upwind Scheme for Ideal and Shallow Water Magnetohydrodynamics.
CoRR, 2022

Provably Positive Central DG Schemes via Geometric Quasilinearization for Ideal MHD Equations.
CoRR, 2022

2021
Uniformly High-Order Structure-Preserving Discontinuous Galerkin Methods for Euler Equations with Gravitation: Positivity and Well-Balancedness.
SIAM J. Sci. Comput., 2021

Minimum Principle on Specific Entropy and High-Order Accurate Invariant-Region-Preserving Numerical Methods for Relativistic Hydrodynamics.
SIAM J. Sci. Comput., 2021

Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations.
Numerische Mathematik, 2021

2020
Entropy Symmetrization and High-Order Accurate Entropy Stable Numerical Schemes for Relativistic MHD Equations.
SIAM J. Sci. Comput., 2020

Structure-Preserving Method for Reconstructing Unknown Hamiltonian Systems From Trajectory Data.
SIAM J. Sci. Comput., 2020

Methods to Recover Unknown Processes in Partial Differential Equations Using Data.
J. Sci. Comput., 2020

Data-driven deep learning of partial differential equations in modal space.
J. Comput. Phys., 2020

A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data.
CoRR, 2020

2019
Provably positive high-order schemes for ideal magnetohydrodynamics: analysis on general meshes.
Numerische Mathematik, 2019

Numerical aspects for approximating governing equations using data.
J. Comput. Phys., 2019

Data driven governing equations approximation using deep neural networks.
J. Comput. Phys., 2019

2018
A Provably Positive Discontinuous Galerkin Method for Multidimensional Ideal Magnetohydrodynamics.
SIAM J. Sci. Comput., 2018

Positivity-Preserving Analysis of Numerical Schemes for Ideal Magnetohydrodynamics.
SIAM J. Numer. Anal., 2018

Sequential function approximation on arbitrarily distributed point sets.
J. Comput. Phys., 2018

Sequential function approximation with noisy data.
J. Comput. Phys., 2018

An Explicit Neural Network Construction for Piecewise Constant Function Approximation.
CoRR, 2018

2017
A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation.
SIAM J. Sci. Comput., 2017

A stochastic Galerkin method for first-order quasilinear hyperbolic systems with uncertainty.
J. Comput. Phys., 2017

2016
A Direct Eulerian GRP Scheme for Spherically Symmetric General Relativistic Hydrodynamics.
SIAM J. Sci. Comput., 2016

2015
High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics.
J. Comput. Phys., 2015

2014
A third-order accurate direct Eulerian GRP scheme for the Euler equations in gas dynamics.
J. Comput. Phys., 2014

Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics.
J. Comput. Phys., 2014


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