Kailiang Wu

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 67

Top Topics

Numerical Scheme

Top Venues

J. Comput. Phys.

SIAM J. Sci. Comput.

SIAM J. Numer. Anal.

Publications

Chaoyi Cai, Jianxian Qiu, Kailiang Wu
Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics. J. Comput. Phys. 498 (2024)
Shengrong Ding, Kailiang Wu
GQL-based bound-preserving and locally divergence-free central discontinuous Galerkin schemes for relativistic magnetohydrodynamics. J. Comput. Phys. 514 (2024)
Shumo Cui, Alexander Kurganov, Kailiang Wu
Bound-Preserving Framework for Central-Upwind Schemes for General Hyperbolic Conservation Laws. CoRR (2024)
Jiangfu Wang, Huazhong Tang, Kailiang Wu
High-order accurate positivity-preserving and well-balanced discontinuous Galerkin schemes for ten-moment Gaussian closure equations with source terms. CoRR (2024)
Alina Chertock, Alexander Kurganov, Michael Redle, Kailiang Wu
A New Locally Divergence-Free Path-Conservative Central-Upwind Scheme for Ideal and Shallow Water Magnetohydrodynamics. SIAM J. Sci. Comput. 46 (3) (2024)
Mengqing Liu, Kailiang Wu
Structure-Preserving Oscillation-Eliminating Discontinuous Galerkin Schemes for Ideal MHD Equations: Locally Divergence-Free and Positivity-Preserving. CoRR (2024)
Shengrong Ding, Kailiang Wu
A New Discretely Divergence-Free Positivity-Preserving High-Order Finite Volume Method for Ideal MHD Equations. SIAM J. Sci. Comput. 46 (1) (2024)
Chaoyi Cai, Jianxian Qiu, Kailiang Wu
Provably Convergent and Robust Newton-Raphson Method: A New Dawn in Primitive Variable Recovery for Relativistic MHD. CoRR (2024)
Shumo Cui, Shengrong Ding, Kailiang Wu
On Optimal Cell Average Decomposition for High-Order Bound-Preserving Schemes of Hyperbolic Conservation Laws. SIAM J. Numer. Anal. 62 (2) (2024)
Wei Chen, Kailiang Wu, Tao Xiong
High Order Structure-Preserving Finite Difference WENO Schemes for MHD Equations with Gravitation in all Sonic Mach Numbers. J. Sci. Comput. 99 (2) (2024)
Shengrong Ding, Kailiang Wu
GQL-Based Bound-Preserving and Locally Divergence-Free Central Discontinuous Galerkin Schemes for Relativistic Magnetohydrodynamics. CoRR (2024)
Ce Zhang, Kailiang Wu, Zhihai He
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems. IEEE Trans. Artif. Intell. 5 (6) (2024)
Linfeng Xu, Shengrong Ding, Kailiang Wu
High-Order Accurate Entropy Stable Schemes for Relativistic Hydrodynamics with General Synge-Type Equation of State. J. Sci. Comput. 98 (2) (2024)
Junfeng Chen, Kailiang Wu
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning. CoRR (2024)
Shengrong Ding, Kailiang Wu
A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations. CoRR (2023)
Wei Chen, Kailiang Wu, Tao Xiong
High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers. J. Comput. Phys. 488 (2023)
Yupeng Ren, Kailiang Wu, Jianxian Qiu, Yulong Xing
On high order positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation. J. Comput. Phys. 492 (2023)
Junfeng Chen, Kailiang Wu
Deep-OSG: Deep learning of operators in semigroup. J. Comput. Phys. 493 (2023)
Junfeng Chen, Kailiang Wu
Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems. CoRR (2023)
Kailiang Wu, Haili Jiang, Chi-Wang Shu
Provably Positive Central Discontinuous Galerkin Schemes via Geometric Quasilinearization for Ideal MHD Equations. SIAM J. Numer. Anal. 61 (1) (2023)
Ce Zhang, Kailiang Wu, Zhihai He
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems. CoRR (2023)
Shumo Cui, Shengrong Ding, Kailiang Wu
Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions? J. Comput. Phys. 476 (2023)
Chaoyi Cai, Jianxian Qiu, Kailiang Wu
Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics. CoRR (2023)
Kailiang Wu, Chi-Wang Shu
Geometric Quasilinearization Framework for Analysis and Design of Bound-Preserving Schemes. SIAM Rev. 65 (4) (2023)
Manting Peng, Zheng Sun, Kailiang Wu
OEDG: Oscillation-eliminating discontinuous Galerkin method for hyperbolic conservation laws. CoRR (2023)
Shumo Cui, Shengrong Ding, Kailiang Wu
On Optimal Cell Average Decomposition for High-Order Bound-Preserving Schemes of Hyperbolic Conservation Laws. CoRR (2022)
Alina Chertock, Alexander Kurganov, Michael Redle, Kailiang Wu
A New Locally Divergence-Free Path-Conservative Central-Upwind Scheme for Ideal and Shallow Water Magnetohydrodynamics. CoRR (2022)
Haili Jiang, Huazhong Tang, Kailiang Wu
Positivity-Preserving Well-Balanced Central Discontinuous Galerkin Schemes for the Euler Equations under Gravitational Fields. CoRR (2022)
Yaping Chen, Kailiang Wu
A Physical-Constraint-Preserving Finite Volume WENO Method for Special Relativistic Hydrodynamics on Unstructured Meshes. CoRR (2022)
Wei Chen, Kailiang Wu, Tao Xiong
High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers. CoRR (2022)
Haili Jiang, Huazhong Tang, Kailiang Wu
Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields. J. Comput. Phys. 463 (2022)
Zheng Sun, Yuanzhe Wei, Kailiang Wu
On Energy Laws and Stability of Runge-Kutta Methods for Linear Seminegative Problems. CoRR (2022)
Zheng Sun, Yuanzhe Wei, Kailiang Wu
On Energy Laws and Stability of Runge-Kutta Methods for Linear Seminegative Problems. SIAM J. Numer. Anal. 60 (5) (2022)
Shumo Cui, Shengrong Ding, Kailiang Wu
Is the Classic Convex Decomposition Optimal for Bound-Preserving Schemes in Multiple Dimensions? CoRR (2022)
Kailiang Wu, Haili Jiang, Chi-Wang Shu
Provably Positive Central DG Schemes via Geometric Quasilinearization for Ideal MHD Equations. CoRR (2022)
Zhen Chen, Victor Churchill, Kailiang Wu, Dongbin Xiu
Deep neural network modeling of unknown partial differential equations in nodal space. J. Comput. Phys. 449 (2022)
Yaping Chen, Kailiang Wu
A physical-constraint-preserving finite volume WENO method for special relativistic hydrodynamics on unstructured meshes. J. Comput. Phys. 466 (2022)
Zhen Chen, Victor Churchill, Kailiang Wu, Dongbin Xiu
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space. CoRR (2021)
Kailiang Wu
Minimum Principle on Specific Entropy and High-Order Accurate Invariant Region Preserving Numerical Methods for Relativistic Hydrodynamics. CoRR (2021)
Kailiang Wu, Chi-Wang Shu
Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations. Numerische Mathematik 148 (3) (2021)
Kailiang Wu, Chi-Wang Shu
Geometric Quasilinearization Framework for Analysis and Design of Bound-Preserving Schemes. CoRR (2021)
Kailiang Wu, Yulong Xing
Uniformly High-Order Structure-Preserving Discontinuous Galerkin Methods for Euler Equations with Gravitation: Positivity and Well-Balancedness. SIAM J. Sci. Comput. 43 (1) (2021)
Kailiang Wu
Minimum Principle on Specific Entropy and High-Order Accurate Invariant-Region-Preserving Numerical Methods for Relativistic Hydrodynamics. SIAM J. Sci. Comput. 43 (6) (2021)
Kailiang Wu, Chi-Wang Shu
Provably Physical-Constraint-Preserving Discontinuous Galerkin Methods for Multidimensional Relativistic MHD Equations. CoRR (2020)
Kailiang Wu, Chi-Wang Shu
Entropy Symmetrization and High-Order Accurate Entropy Stable Numerical Schemes for Relativistic MHD Equations. SIAM J. Sci. Comput. 42 (4) (2020)
Kailiang Wu, Dongbin Xiu
Data-driven deep learning of partial differential equations in modal space. J. Comput. Phys. 408 (2020)
Kailiang Wu, Yulong Xing
Uniformly High-Order Structure-Preserving Discontinuous Galerkin Methods for Euler Equations with Gravitation: Positivity and Well-Balancedness. CoRR (2020)
Zhen Chen, Kailiang Wu, Dongbin Xiu
Methods to Recover Unknown Processes in Partial Differential Equations Using Data. J. Sci. Comput. 85 (2) (2020)
Zhen Chen, Kailiang Wu, Dongbin Xiu
Methods to Recover Unknown Processes in Partial Differential Equations Using Data. CoRR (2020)
Jun Hou, Tong Qin, Kailiang Wu, Dongbin Xiu
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data. CoRR (2020)
Kailiang Wu, Tong Qin, Dongbin Xiu
Structure-Preserving Method for Reconstructing Unknown Hamiltonian Systems From Trajectory Data. SIAM J. Sci. Comput. 42 (6) (2020)
Kailiang Wu, Chi-Wang Shu
Entropy Symmetrization and High-Order Accurate Entropy Stable Numerical Schemes for Relativistic MHD Equations. CoRR (2019)
Tong Qin, Kailiang Wu, Dongbin Xiu
Data driven governing equations approximation using deep neural networks. J. Comput. Phys. 395 (2019)
Kailiang Wu, Dongbin Xiu
Data-Driven Deep Learning of Partial Differential Equations in Modal Space. CoRR (2019)
Kailiang Wu, Chi-Wang Shu
Provably positive high-order schemes for ideal magnetohydrodynamics: analysis on general meshes. Numerische Mathematik 142 (4) (2019)
Kailiang Wu, Tong Qin, Dongbin Xiu
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data. CoRR (2019)
Kailiang Wu, Dongbin Xiu
Numerical aspects for approximating governing equations using data. J. Comput. Phys. 384 (2019)
Kailiang Wu, Dongbin Xiu
An Explicit Neural Network Construction for Piecewise Constant Function Approximation. CoRR (2018)
Yeonjong Shin, Kailiang Wu, Dongbin Xiu
Sequential function approximation with noisy data. J. Comput. Phys. 371 (2018)
Kailiang Wu, Dongbin Xiu
Numerical Aspects for Approximating Governing Equations Using Data. CoRR (2018)
Kailiang Wu, Chi-Wang Shu
A Provably Positive Discontinuous Galerkin Method for Multidimensional Ideal Magnetohydrodynamics. SIAM J. Sci. Comput. 40 (5) (2018)
Kailiang Wu, Dongbin Xiu
Sequential function approximation on arbitrarily distributed point sets. J. Comput. Phys. 354 (2018)
Tong Qin, Kailiang Wu, Dongbin Xiu
Data Driven Governing Equations Approximation Using Deep Neural Networks. CoRR (2018)
Kailiang Wu
Positivity-Preserving Analysis of Numerical Schemes for Ideal Magnetohydrodynamics. SIAM J. Numer. Anal. 56 (4) (2018)
Kailiang Wu, Yeonjong Shin, Dongbin Xiu
A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation. SIAM J. Sci. Comput. 39 (5) (2017)
Kailiang Wu, Huazhong Tang, Dongbin Xiu
A stochastic Galerkin method for first-order quasilinear hyperbolic systems with uncertainty. J. Comput. Phys. 345 (2017)
Kailiang Wu, Huazhong Tang
A Direct Eulerian GRP Scheme for Spherically Symmetric General Relativistic Hydrodynamics. SIAM J. Sci. Comput. 38 (3) (2016)
Kailiang Wu, Huazhong Tang
High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics. J. Comput. Phys. 298 (2015)
Kailiang Wu, Zhicheng Yang, Huazhong Tang
A third-order accurate direct Eulerian GRP scheme for the Euler equations in gas dynamics. J. Comput. Phys. 264 (2014)
Kailiang Wu, Huazhong Tang
Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics. J. Comput. Phys. 256 (2014)