人员 > 科研教学系列 > 吴开亮

吴开亮

副教授  

https://faculty.sustech.edu.cn/wukl

  • 简历
  • 科研
  • 教学
  • 发表论著

吴开亮,籍贯安徽省安庆市,南方科技大学数学系/深圳国际数学中心/深圳国家应用数学中心副教授、研究员、博士生导师。在华中科技大学获数学学士学位,在北京大学获计算数学博士学位;先后在美国犹他大学和俄亥俄州立大学从事博士后研究。

     致力于研究偏微分方程数值解、机器学习与数据驱动建模、计算流体力学与数值相对论等。与合作者在高精度保结构数值方法及其理论分析方面做出了一系列工作:提出了几何拟线性化(GQL)框架,为研究含非线性约束的复杂保界问题提供了新途径;发展严谨理论,揭示了可压磁流体数值方法的保界/保正性与磁场零散度条件之间的深层联系,建立了可压磁流体的高精度保界/保正框架,解决了该方向的公开难题,被国际同行称为"迄今"实现磁流体高阶格式保界性的"唯一途径",被美国《数学评论》称为解决了"a highly desirable task"、"a challenge";系统地发展了狭义和广义相对论流体力学的保物理约束(PCP)方法,被物理学家称为“在一般时空中,确保流体变量物理性的一种通用方法”;基于凸几何和对称群理论,系统发展了高效保界算法的最优均值分解理论,揭示了均值分解与非负多项式锥上的正线性泛函之间的数学联系,解决了经典均值分解是否最优的猜测。发展了一套高维函数序列逼近算法及其误差分析。基于深度学习构建了推演数据中蕴藏的未知数学方程/模型的新框架,并在算子学习方面取得新进展。

     成果发表在SIAM系列SIAM Review (Spotlight)/SIAM J. Numer. Anal./SIAM J. Sci. Comput. (15篇)、J. Comput. Phys. (18篇)、Math. Comp.、Numer. Math.、M3AS、天体物理权威期刊ApJS、Phys. Rev. D、人工智会议/期刊 ICML、IEEE Trans. Artif. Intell. 等。曾获中国数学会 钟家庆数学奖(2019);入选国家高层次青年人才计划;主持国家自然科学基金 重大研究计划培育项目和面上项目、深圳市优秀人才(杰青)项目。


研究领域

微分方程数值解、机器学习与数据科学、计算流体力学、计算物理、高维逼近论与不确定性量化


◆ 吴开亮研究团队目前有研究助理教授(RAP)2名、博士后6名、博士/硕士研究生共5名。
每年计划招收优秀博士生/硕士生 1-2名。

◆ 长期招聘优秀博士后(年薪34W+;租房补贴3.36W/年);(曾)指导RAP/博士后8名,来自清华大学、中国科学院大学、中国科学技术大学、武汉大学、厦门大学、湘潭大学、香港中文大学、法国巴黎文理研究大学(全球前50)等国内外高校。

有意者请将材料发送至 WUKL@sustech.edu.cn

详情请见:https://faculty.sustech.edu.cn/?cat=11&tagid=wukl&orderby=date&iscss=1&snapid=1


奖励及荣誉

◆ 2023:最受2023届数学系本科毕业生喜爱的老师

◆ 2023:深圳市优秀人才项目(杰青项目)

◆ 2020:国家高层次人才计划(青年项目)

◆ 2019:中国数学会 钟家庆数学奖

◆ 2015:中国数学会计算数学分会 优秀青年论文奖一等奖

◆ 2014:北京大学 “挑战杯”五四青年科学奖一等奖


代表性论文 


◆ K. Wu

Positivity-preserving analysis of numerical schemes for ideal magnetohydrodynamics
SIAM Journal on Numerical Analysis,   56(4):2124--2147, 2018.


◆ K. Wu and C.-W. Shu
Geometric quasilinearization framework for analysis and design of bound-preserving schemes
SIAM Review,   (Research Spotlight)   65(4): 1031--1073, 2023.  


◆ K. Wu* and C.-W. Shu

Provably positive high-order schemes for ideal magnetohydrodynamics: Analysis on general meshes

Numerische Mathematik,   142(4): 995--1047, 2019.


◆ K. Wu 

Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics

SIAM Journal on Scientific Computing,   43(6): B1164--B1197, 2021.


◆ K. Wu

Design of provably physical-constraint-preserving methods for general relativistic hydrodynamics

Physical Review D,   95, 103001, 2017. 


◆ K. Wu and H. Tang

Admissible states and physical-constraints-preserving schemes for relativistic magnetohydrodynamic equations

Math. Models Methods Appl. Sci. (M3AS),   27(10):1871--1928, 2017.


◆ K. Wu and D. Xiu

Data-driven deep learning of partial differential equations in modal space

Journal of Computational Physics,   408: 109307, 2020.


◆ K. Wu*, H. Jiang, and C.-W. Shu
Provably positive central discontinuous Galerkin schemes via geometric quasilinearization for ideal MHD equations
SIAM Journal on Numerical Analysis,   61: 250--285, 2023.


◆ Z. Sun, Y. Wei, and K. Wu*

On energy laws and stability of Runge--Kutta methods for linear seminegative problems

SIAM Journal on Numerical Analysis,   60(5): 2448--2481, 2022.


◆ S. Cui, S. Ding, and K. Wu*

On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws

SIAM Journal on Numerical Analysis,   accepted,  2023.


◆ K. Wu* and C.-W. Shu

Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations

Numerische Mathematik,   148: 699--741, 2021.


◆ K. Wu and C.-W. Shu

A provably positive discontinuous Galerkin method for multidimensional ideal magnetohydrodynamics

SIAM Journal on Scientific Computing,   40(5):B1302--B1329, 2018.


◆ K. Wu and C.-W. Shu

Entropy symmetrization and high-order accurate entropy stable numerical schemes for relativistic MHD equations

SIAM Journal on Scientific Computing,   42(4): A2230--A2261, 2020. 


◆ K. Wu, Y. Shin, and D. Xiu

A randomized tensor quadrature method for high dimensional polynomial approximation

SIAM Journal on Scientific Computing,   39(5):A1811--A1833, 2017.


◆ K. Wu and Y. Xing

Uniformly high-order structure-preserving discontinuous Galerkin methods for Euler equations with gravitation: Positivity and well-balancedness

SIAM Journal on Scientific Computing,    43(1): A472--A510, 2021.


◆ K. Wu, T. Qin, and D. Xiu

Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data

SIAM Journal on Scientific Computing,   42(6): A3704--A3729, 2020. 


◆ S. Ding and K.Wu*

A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations

SIAM Journal on Scientific Computing,   accepted, 2023.


◆ S. Cui, A. Kurganov, and K. Wu*

Bound-preserving framework for central-upwind schemes for general hyperbolic conservation laws

SIAM Journal on Scientific Computing,  accepted, 2024.


◆ K. Wu and H. Tang

A direct Eulerian GRP scheme for spherically symmetric general relativistic hydrodynamics

SIAM Journal on Scientific Computing,   38(3):B458--B489, 2016. 


◆  A. Chertock, A. Kurganov, M. Redle, and K. Wu
A new locally divergence-free path-conservative central-upwind scheme for ideal and shallow water magnetohydrodynamics
SIAM Journal on Scientific Computing,   accepted, 2024.


◆ K. Wu and H. Tang

High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics

Journal of Computational Physics,   298:539--564, 2015.


◆ T. Qin, K. Wu, and D. Xiu

Data driven governing equations approximation using deep neural networks

Journal of Computational Physics,   395: 620--635, 2019.


◆ S. Cui, S. Ding, and K. Wu*
Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?
Journal of Computational Physics,   476: 111882,  2022.


◆  J. Chen and K. Wu*
Deep-OSG: Deep learning of operators in semigroup
Journal of Computational Physics,   accepted, 2023.


◆ K. Wu and H. Tang

Physical-constraint-preserving central discontinuous Galerkin methods for special relativistic hydrodynamics with a general equation of state

Astrophys. J. Suppl. Ser. (ApJS),   228(1):3(23pages), 2017. (2015 Impact Factor of ApJS: 11.257)


◆  S. Ding and K. Wu*

GQL-based bound-preserving and locally divergence-free central discontinuous Galerkin schemes for relativistic magnetohydrodynamics

Journal of Computational Physics,   accepted, 2024.


◆ W. Chen, K. Wu, and T. Xiong
High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers
Journal of Computational Physics,    488: 112240, 2023.


◆ C. Cai, J. Qiu, and K.Wu*

Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics

Journal of Computational Physics,   accepted, 2023.


◆ Z. Chen, V. Churchill, K. Wu, and D. Xiu

Deep neural network modeling of unknown partial differential equations in nodal space

Journal of Computational Physics,     449: 110782, 2022.


◆ Y. Chen and K. Wu*

A physical-constraint-preserving finite volume method for special relativistic hydrodynamics on unstructured meshes

Journal of Computational Physics,    466: 111398, 2022.


◆ H. Jiang, H. Tang, and K. Wu*

Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields

Journal of Computational Physics,     463: 111297, 2022.


◆ Z. Chen, K. Wu, and D. Xiu

Methods to recover unknown processes in partial differential equations using data

Journal of Scientific Computing,   85:23, 2020. 


◆ K. Wu and D. Xiu

Numerical aspects for approximating governing equations using data

Journal of Computational Physics,   384: 200--221, 2019.


◆ Y. Shin, K. Wu, and D. Xiu

Sequential function approximation with noisy data

Journal of Computational Physics,   371:363--381, 2018.


◆ Y. Ren, K. Wu, J. Qiu, and Y. Xing 

On positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation

Journal of Computational Physics,  accepted, 2023.


◆ K. Wu and D. Xiu

Sequential function approximation on arbitrarily distributed point sets

Journal of Computational Physics,   354:370--386, 2018.


◆ K. Wu and H. Tang

On physical-constraints-preserving schemes for special relativistic magnetohydrodynamics with a general equation of state

Z. Angew. Math. Phys.,   69:84(24pages), 2018.


◆ K. Wu, H. Tang, and D. Xiu

A stochastic Galerkin method for first-order quasilinear hyperbolic systems with uncertainty

Journal of Computational Physics,   345:224--244, 2017. 


◆ K. Wu and H. Tang

Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics

Journal of Computational Physics,   256:277--307, 2014. 


◆ K. Wu, Z. Yang, and H. Tang

A third-order accurate direct Eulerian GRP scheme for the Euler equations in gas dynamics

Journal of Computational Physics,   264:177--208, 2014. 


◆ J. Chen and K. Wu*

Positional knowledge is all you need: Position-induced Transformer (PiT) for operator learning
International Conference on Machine Learning (ICML),   2024.



学术服务

◆ 期刊编委 Journal on Numerical Methods and Computer Applications (数值计算与计算机应用)

◆ 期刊编委 Frontiers in Applied Mathematics and Statistics (Numerical Analysis and Scientific Computation Section)

◆ 美国《数学评论》评论员

◆ 下列学术期刊和机器学习会议审稿人

  • Applied Mathematics and Computation
  • Applied Numerical Mathematics
  • Chinese Journal of Theoretical and Applied Mechanics
  • Communications in Computational Physics
  • Computer Methods in Applied Mechanics and Engineering
  • Computers and Mathematics with Applications
  • East Asian Journal on Applied Mathematics
  • Electronic Research Archive
  • Engineering Optimization
  • Journal of Computational and Applied Mathematics
  • Journal of Computational Physics   ( 70+ times )
  • Journal of Numerical Mathematics
  • Journal of Scientific Computing
  • Journal of Mathematical Biology
  • Journal of Applied Mathematics and Computing
  • Mathematical Models and Methods in Applied Sciences (M3AS)
  • Mathematica Numerica Sinica
  • Mathematics of Computation (MCOM)
  • Numerical Methods for Partial Differential Equations
  • SIAM Journal on Scientific Computing (SISC)
  • SIAM/ASA Journal on Uncertainty Quantification
  • Theoretical and Applied Mechanics Letters
  • International Conference on Machine Learning (ICML)



保结构数值方法及其理论:保持正性、物理约束、平衡性、熵稳定、能量稳定/耗散、最小熵原理、渐近性、磁场零散度等结构

K. Wu, Positivity-preserving analysis of numerical schemes for ideal magnetohydrodynamics, SIAM Journal on Numerical Analysis, 2018.

K. Wu and C.-W. Shu, Geometric quasilinearization framework for analysis and design of bound-preserving schemes, SIAM Review, 2023.

K. Wu* and C.-W. Shu, Provably positive high-order schemes for ideal magnetohydrodynamics: Analysis on general meshes, Numerische Mathematik, 2019.

K. Wu, Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics, SIAM Journal on Scientific Computing,  2021.

K. Wu*, H. Jiang, and C.-W. Shu, Provably positive central discontinuous Galerkin schemes via geometric quasilinearization for ideal MHD equations, SIAM Journal on Numerical Analysis, 2023.

S. Cui, S. Ding, and K. Wu*, On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws, SIAM Journal on Numerical Analysis, 2024.

Z. Sun, Y. Wei, and K. Wu*,  On energy laws and stability of Runge--Kutta methods for linear seminegative problems, SIAM Journal on Numerical Analysis, 2022.

K. Wu* and C.-W. Shu, Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations, Numerische Mathematik, 2021.

K. Wu and H. Tang, Admissible states and physical-constraints-preserving schemes for relativistic magnetohydrodynamic equations, Math. Models Methods Appl. Sci. (M3AS), 2017.

K. Wu and Y. Xing, Uniformly high-order structure-preserving discontinuous Galerkin methods for Euler equations with gravitation: Positivity and well-balancedness, SIAM Journal on Scientific Computing,  2021.

K. Wu and C.-W. Shu, Entropy symmetrization and high-order accurate entropy stable numerical schemes for relativistic MHD equations, SIAM Journal on Scientific Computing, 2020.

K. Wu and C.-W. Shu, A provably positive discontinuous Galerkin method for multidimensional ideal magnetohydrodynamics, SIAM Journal on Scientific Computing, 2018.

K. Wu, Design of provably physical-constraint-preserving methods for general relativistic hydrodynamics, Physical Review D, 2017.

K. Wu and H. Tang, High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics, Journal of Computational Physics, 2015.

H. Jiang, H. Tang, and K. Wu*, Positivity-preserving well-balanced central discontinuous Galekin schemes for the Euler equations under gravitational fields, Journal of Computational Physics, 2022.

S. Cui, S. Ding, and K. Wu*, Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?, Journal of Computational Physics, 2023.

A. Chertock, A. Kurganov, M. Redle, and K. Wu, A new locally divergence-free path-conservative central-upwind scheme for ideal and shallow water magnetohydrodynamics, preprint, 2022.
W. Chen, K. Wu, and T. Xiong, High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers, Journal of Computational Physics, 2023.

Y. Ren, K. Wu, J. Qiu, and Y. Xing, On high-order positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation, Journal of Computational Physics, 2023. 

C. Cai, J. Qiu, and K.Wu*, Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics, Journal of Computational Physics, 2024.

L. Xu, S. Ding, and K. Wu*, High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state, Journal of Scientific Computing, 2024. 

S. Ding and K. Wu*, A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations, SIAM Journal on Scientific Computing, 2024.

W. Chen, K. Wu, and T. Xiong, High order structure-preserving finite difference WENO scheme for MHD equations with gravitation in all sonic Mach numbers, Journal of Scientific Computing, 2024. 
S. Cui, A. Kurganov, and K. Wu*, Bound-preserving framework for central-upwind schemes for general hyperbolic conservation laws, preprint, 2024.
C. Cai, J. Qiu, and K. Wu*, Efficient provably convergent Newton-Raphson method for primitive variables in relativistic MHD equations, preprint, 2024.


深度学习、数据驱动建模

J. Chen and K. Wu*, Positional knowledge is all you need: Position-induced Transformer (PiT) for operator learning, International Conference on Machine Learning (ICML), 2024.

J. Chen and K. Wu*, Deep-OSG: Deep learning of operators in semigroup, Journal of Computational Physics,  2023.

C. Zhang, K. Wu, and Z. He, Critical sampling for robust evolution operator learning of unknown dynamical systems, IEEE Transactions on Artificial Intelligence, 2024.

K.Wu and D. Xiu, Data-driven deep learning of partial differential equations in modal space, Journal of Computational Physics, 2020.

K.Wu and D. Xiu, Data-driven deep learning of partial differential equations in modal space, Journal of Computational Physics, 2020.

T. Qin, K. Wu, and D. Xiu, Data driven governing equations approximation using deep neural networks, Journal of Computational Physics, 2019.

K. Wu, T. Qin, and D. Xiu, Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data, SIAM Journal on Scientific Computing, 2020.

Z. Chen, V. Churchill, K. Wu, and D. Xiu, Deep neural network modeling of unknown partial differential equations in nodal space, Journal of Computational Physics, 2022.

Z. Chen, K. Wu, and D. Xiu, Methods to recover unknown processes in partial differential equations using data, Journal of Scientific Computing, 2020.

J. Hou, T. Qin, K. Wu and D. Xiu,  A non-intrusive correction algorithm for classification problems with corrupted data, Commun. Appl. Math. Comput., 2020.

K. Wu and D. Xiu, Numerical aspects for approximating governing equations using data, Journal of Computational Physics, 2019.


相对论流体力学方程的数学性质与高阶精度数值方法

K. Wu, Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics, SIAM Journal on Scientific Computing,  2021.

K. Wu, Design of provably physical-constraint-preserving methods for general relativistic hydrodynamics, Physical Review D, 2017.

K. Wu and H. Tang, High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics, Journal of Computational Physics, 2015.

K. Wu and H. Tang, Admissible states and physical-constraints-preserving schemes for relativistic magnetohydrodynamic equations, Math. Models Methods Appl. Sci. (M3AS), 2017.

K. Wu* and C.-W. Shu, Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations, Numerische Mathematik, 2021.

K. Wu and H. Tang, A direct Eulerian GRP scheme for spherically symmetric general relativistic hydrodynamics, SIAM Journal on Scientific Computing, 2016.

K. Wu and H. Tang, Physical-constraint-preserving central discontinuous Galerkin methods for special relativistic hydrodynamics with a general equation of state, Astrophys. J. Suppl. Ser. (ApJS), 2017.

K. Wu and H. Tang, Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics, Journal of Computational Physics, 2014.

Y. Chen and K. Wu*, A physical-constraint-preserving finite volume method for special relativistic hydrodynamics on unstructured meshes, Journal of Computational Physics, 2022.

L. Xu, S. Ding, and K. Wu*, High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state, Journal of Scientific Computing, 2024. 

C. Cai, J. Qiu, and K.Wu*, Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics, Journal of Computational Physics, 2024.

C. Cai, J. Qiu, and K. Wu*, Efficient provably convergent Newton-Raphson method for primitive variables in relativistic MHD equations, preprint, 2024.


高维函数逼近:大数据样本、最优采样、多项式逼近

K. Wu, Y. Shin, and D. XiuA randomized tensor quadrature method for high dimensional polynomial approximationSIAM Journal on Scientific Computing, 2017.

K. Wu and D. XiuSequential function approximation on arbitrarily distributed point setsJournal of Computational Physics, 2018.

Y. Shin, K. Wu, and D. Xiu, Sequential function approximation with noisy data, Journal of Computational Physics, 2018.

K. Wu and D. Xiu,  Sequential approximation of functions in Sobolev spaces using random samples, Commun. Appl. Math. Comput., 2019.


双曲守恒律方程的(广义)黎曼解Godunov型数值方法

K. Wu and H. Tang, A direct Eulerian GRP scheme for spherically symmetric general relativistic hydrodynamics, SIAM Journal on Scientific Computing, 2016.

K. Wu and H. Tang, Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics, Journal of Computational Physics, 2014.

K. Wu, Z. Yang, and H. Tang, A third-order accurate direct Eulerian GRP scheme for the Euler equations in gas dynamics, Journal of Computational Physics, 2014.


不确定性量化、随机Galerkin方法

K. Wu, D. Xiu, and X. Zhong, A WENO-based stochastic Galerkin scheme for ideal MHD equations with random inputs, Communications in Computational Physics, 2021.

K. Wu, H. Tang, and D. Xiu, A stochastic Galerkin method for first-order quasilinear hyperbolic systems with uncertainty, Journal of Computational Physics, 2017. 

Teaching(授课课程)

Fall Semester:Mathematical Experiments 数学实验(本科生)

Spring Semester:Computational Fluid Dynamics and Deep Learning 计算流体力学与深度学习(本研)

2021 Spring Semester:Calculus II 高等数学II(本科生)

2022 Spring Semester:Advanced Topics in Modern Computational Mathematics (本科生)


Group Members (课题组成员)


Research Assistant Professor(研究助理教授)

  • Dr. Shumo Cui (2023.2.1-):  Ph.D. from Tulane University;  Postdoc at Temple University; VAP at SUSTech. We have a joint article: "Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?" published in 《Journal of Computational Physics》. We have a joint article: "On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws" accepted for publication in 《SIAM Journal on Numerical Analysis》. We have a joint article with Prof. Alexander Kurganov: "Bound-preserving framework for central-upwind schemes for general hyperbolic conservation laws" submitted to SISC. 
  • Dr. Shengrong Ding (2023.12-): Ph.D. from University of Science and Technology of China(中科大博士); Postdoc at SUSTech(南科大博士后). We have a joint article: "Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?" published in 《Journal of Computational Physics》. We have a joint article: "On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws" accepted for publication in 《SIAM Journal on Numerical Analysis》. We have a joint article "A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations" accepted for publication in 《SIAM Journal on Scientific Computing》. We have a joint article: "High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state" published in《Journal of Scientific Computing》.


Postdoctoral Fellows(博士后研究员)

  • Dr. Shengrong Ding (2021.11-2023.11): Ph.D. from University of Science and Technology of China(中科大博士). We have a joint article: "Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?" published in 《Journal of Computational Physics》. We have a joint article: "On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws" accepted for publication in 《SIAM Journal on Numerical Analysis》. We have a joint article "A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations" accepted for publication in 《SIAM Journal on Scientific Computing》. We have a joint article: "High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state" published in《Journal of Scientific Computing》.

  • Dr. Junfeng Chen (Postdoc Fellow: 2022.10-present; Visiting Postdoc Scholar: 2022.03-2022.09): B.Sc. from Tsinghua University(清华大学本科),Ph.D. from Paris Sciences et Lettres – PSL Research University(法国巴黎文理研究大学博士). 入选 "海外引才专项" 和 "广东省海外青年博士后引进项目". We have a joint article "Deep-OSG: Deep learning of operators in semigroup" published in《Journal of Computational Physics》. We have a joint article "Positional knowledge is all you need: Position-induced Transformer (PiT) for operator learning" accepted for publication in 《International Conference on Machine Learning (ICML)》. 

  • Dr. Ruifang Yan (Postdoc Fellow: 2023.07-): Ph.D. from Wuhan University(武汉大学博士). 

  • Dr. Huihui Cao (Postdoc Fellow: 2023.07-): Ph.D. from Xiangtan University(湘潭大学博士).

  • Dr. Chuan Fan (Postdoc Fellow: 2023.09-; Visiting Postdoc Scholar: 2023.06-2023.09): Ph.D. from Xiamen University(厦门大学博士).

  • Dr. Mengqing Liu (Postdoc Fellow: 2023.09-): Ph.D. from University of Chinese Academy of Sciences(中国科学院大学博士). We have a joint article "Structure-preserving oscillation-eliminating discontinuous Galerkin schemes for ideal MHD equations: Locally divergence-free and positivity-preserving" submitted.

  • Dr. Qinghe Wang (Postdoc Fellow: 2023.12-): Ph.D. from the Chinese University of Hong Kong, Shenzhen(香港中文大学深圳分校博士). 


Graduate Students (研究生)

  • Haili Jiang (2021.04-2021.12),Visiting Ph.D. student from Peking University(北京大学). We have a joint article with Prof. Chi-Wang Shu: "Provably positive central DG schemes via geometric quasilinearization for ideal MHD equations" published in《SIAM Journal on Numerical Analysis》. We have a joint article with Prof. Huazhong Tang: "Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields" published in《Journal of Computational Physics》.

  • Fang Yan (2021.09-2023.07),Master student,B.Sc. from South China University of Technology(华南理工).

  • Zhuoyun Li (2022.09-),Ph.D. student,B.Sc. from SUSTech(南科大).

  • Manting Peng (2022.09-),Master student,B.Sc. from SUSTech(南科大). We have a joint article: "OEDG: Oscillation-eliminating discontinuous Galerkin method for hyperbolic conservation laws" submitted.

  • Linfeng Xu (2022.09-),Master student,B.Sc. from SUSTech(南科大). We have a joint article: "High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state" accepted for publication in《Journal of Scientific Computing》.

  • Dongwen Pang (2023.09-), Ph.D. student, B.Sc. from Wuhan University of Technology(武汉理工), M.Sc. from Xiangtan University(湘潭大学). He is a visiting master student from 2023.03 to 2023.08.

  • Miaosen Jiao (2023.09-), Master student, B.Sc. from SUSTech(南科大).

  • Caiyou Yuan(2023.06.29-08.30), Visiting Ph.D. student from Peking University(北京大学).

  • Zhihao Zhang (2023.06.29-08.30), Visiting Ph.D. student from Peking University(北京大学).

  • Jiangfu Wang (2023.06.29-08.30), Visiting Ph.D. student from Peking University(北京大学). We have a joint article with Prof. Huazhong Tang: "High-order accurate positivity-preserving and well-balanced discontinuous Galerkin schemes for ten-moment Gaussian closure equations with source terms" submitted to 《Journal of Computational Physics》. 


Undergraduate Students (本科生)

  • Yuanzhe Wei: We have a joint article with Prof. Zheng Sun: "On energy laws and stability of Runge-Kutta methods for linear seminegative problems" published in 《SIAM Journal on Numerical Analysis》(计算数学方向的顶级期刊,南科大本科生首次)。He was selected to an exchange study program in MIT(麻省理工)南科大数学系第一位入选MIT交流项目的学生,见报道 [https://mp.weixin.qq.com/s/nhlTvmGpdOrXuwZ-a7v4Tg] . He won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文). He received two offers from MIT and one offer from Brown University(收获麻省理工的2个offer和布朗大学的1个offer); He chooses to pursue his Ph.D. at Brown University(布朗大学)since Sep. 2023.

  • Yunhao Jiang: He was selected into a joint study program in University of Wisconsin-Madison(威斯康星大学麦迪逊分校). He won 3rd class prize in the 2022 International Mathematics Competition for University Students (国际大学生数学竞赛).

  • Zhuoyun Li: 推免研究生(GPA排名并列第一). He won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文).

  • Zepei Liu:推免研究生. He became a master student of Prof. Alexander KURGANOV in Sep. 2022.

  • Manting Peng:推免研究生. She won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文).

  • Mingrui Wang: He pursues his master degree in electronic information at Peking University(北京大学).

  • Linfeng Xu:推免研究生. He won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文).

  • Luowei Yin:He worked on summer research and his bachelor thesis in our group (2021.04-2022.06) and after graduation pursues his Ph.D. at CUHK(香港中文)since Sep. 2022.

  • Zijun Jia:推免研究生. 

  • Yuchen Huang:推免研究生. 

  • Yuanji Zhong:He was selected into a joint study program in University of Wisconsin-Madison(威斯康星大学麦迪逊分校). He won 1st class prize (Guangdong) and 3rd class prize (National) in the 13th Chinese Mathematics Competitions for University Students(全国大学生数学竞赛). He won Silver Award in the 2nd Winter National Mathematical Olympiad for University Students(全国大学生奥林匹克数学竞赛-冬季赛)in 2023.

  • Zhihua Li:He won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文). He pursues his Ph.D. at University of Iowa(爱荷华大学)since Sep. 2023.

  • Miaosheng Jiao:推免研究生. He won SUSTech outstanding bachelor thesis (本科毕业论文入选南科大优秀毕业论文).


Visiting Postdocs(访问博士后)

  • Dr. Hao Li (2023.12-2024.1): Ph.D. from Purdue University;Postdoc at University of Texas at Austin. 

  • Dr. Junming Duan (2023.6-2023.7): Ph.D. Peking University; Postdoc at EPFL. 



Welcome passionate and highly self-motivated students to join our group! If you are interested in our research, please feel free to contact me by email: WUKL@sustech.edu.cn.


招聘博士后和研究助理教授(RAP)。每年计划招收博士生/硕士生 1-2名。

详情请见:https://faculty.sustech.edu.cn/?cat=11&tagid=wukl&orderby=date&iscss=1&snapid=1

有意者请将相关应聘或申请材料发送至:WUKL@sustech.edu.cn


Publications List


[63] Z. Zhang, H. Tang, and K. Wu*

High-order accurate structure-preserving finite volume schemes on adaptive moving meshes for shallow water equations: Well balancedness and positivity

preprint,  2024.


[62] S. Cui, K. Wu*, and L. Xu

Provably Entropy-bounded high-order schemes for relativistic hydrodynamics with Synge-type equation of state

preprint,  2024.


[61] M. Peng and K. Wu*

OECDG: Oscillation-eliminating central discontinuous Galerkin schemes for hyperbolic conservation laws

preprint, 2024.


[60] W. Chen, S. Cui, K. Wu, and T. Xiong

Bound-preserving oscillation-eliminating DG method for adapted pressure Aw-Rascle-Zhang traffic model on networks

preprint, 2024.


[59] C. Fan and K. Wu*

High-order oscillation-eliminating Hermite WENO method for hyperbolic conservation laws

submitted to JCP, 2024. 


[58] Z. Li and K. Wu*

Understanding spectral volume from DG viewpoint: Stability, optimal error estimate, oscillation elimination, and beyond

preprint, 2024. 


[57] S. Ding, S. Cui, and K. Wu*
High-order accurate OEDG schemes with rotational invariance for hyperbolic equations on unstructured meshes

preprint, 2024.


[56] M. Liu and K. Wu*

Structure-preserving oscillation-eliminating discontinuous Galerkin schemes for ideal MHD equations: Locally divergence-free and positivity-preserving

submitted to JCP, 2024.


[55] L. Y. Win, Y. Chen, Y. Nie, L. Cai, and K. Wu

Physical-constraint-preserving high-order finite volume WENO method via geometric quasilinearization for Chaplygin gas dynamics
submitted, 2024.


[54] C. Cai, J. Qiu, and K. Wu*

Provably convergent Newton-Raphson method for recovering primitive variables in relativistic MHD equations

submitted to SIAM Journal on Numerical Analysis, 2024.


[53] J. Wang, H. Tang, K. Wu*

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

submitted to Journal of Computational Physics, 2024.


[52] S. Ding and K. Wu*

GQL-based bound-preserving and locally divergence-free central discontinuous Galerkin schemes for relativistic magnetohydrodynamics

Journal of Computational Physics,   accepted,  2024.          A special issue in honor of Sergei Godunov(俄罗斯科学院院士)


[51] S. Cui, A. Kurganov, and K. Wu*

Bound-preserving framework for central-upwind schemes for general hyperbolic conservation laws

SIAM Journal on Scientific Computing,   accepted, 2024.


[50] M. Peng, Z. Sun, and K.Wu*

OEDG: Oscillation-eliminating discontinuous Galerkin method for hyperbolic conservation laws

Mathematics of Computation,   accepted, 2024.


[49] J. Chen and K. Wu*

Positional knowledge is all you need: Position-induced Transformer (PiT) for operator learning

International Conference on Machine Learning (ICML),   accepted,  2024.


[48] W. Chen, K. Wu, and T. Xiong

High order structure-preserving finite difference WENO scheme for MHD equations with gravitation in all sonic Mach numbers 

Journal of Scientific Computing,  99: 36, 2024. 


[47] A. Chertock, A. Kurganov, M. Redle, and K. Wu

A new locally divergence-free path-conservative central-upwind scheme for ideal and shallow water magnetohydrodynamics

SIAM Journal on Scientific Computing,  accepted, 2024.


[46] C. Cai, J. Qiu, and K.Wu*

Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics

Journal of Computational Physics,   498: 112669, 2024.


[45] C. Zhang, K. Wu, and Z. He

Critical sampling for robust evolution operator learning of unknown dynamical systems

IEEE Transactions on Artificial Intelligence,   in press, 2024.


[44] L. Xu, S. Ding, and K. Wu*

High-order accurate entropy stable schemes for relativistic hydrodynamics with a general equation of state

Journal of Scientific Computing,  98: 43, 2024. 


[43] S. Ding and K.Wu*

A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations

SIAM Journal on Scientific Computing,   46: A50-A79, 2024.


[42] S. Cui, S. Ding, and K. Wu*

On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws

SIAM Journal on Numerical Analysis,   62: 775-810, 2024. 



[41] J. Chen and K. Wu*

Deep-OSG: Deep learning of operators in semigroup

Journal of Computational Physics,   493: 112498, 2023.


[40] Y. Ren, K. Wu, J. Qiu, and Y. Xing

On high-order positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation

Journal of Computational Physics,   492:112429, 2023.


[39] W. Chen, K. Wu, and T. Xiong

High order asymptotic preserving finite difference WENO schemes with constrained transport for MHD equations in all sonic Mach numbers
Journal of Computational Physics,  488: 112240, 2023.


[38] S. Cui, S. Ding, and K. Wu*
Is the classic convex decomposition optimal for bound-preserving schemes in multiple dimensions?
Journal of Computational Physics,   476: 111882, 2023.


[37] K. Wu*, H. Jiang, and C.-W. Shu

Provably positive central DG schemes via geometric quasilinearization for ideal MHD equations
SIAM Journal on Numerical Analysis,   61: 250-285, 2023.


[36] K. Wu and C.-W. Shu
Geometric quasilinearization framework for analysis and design of bound-preserving schemes

SIAM Review (Research Spotlight)  65(4): 1031--1073, 2023.  

    This paper proposes a general approach---Geometric Quasi-Linearization (GQL), motivated by our previous bound-preserving works on MHD and RHD systems.  GQL equivalently transforms nonlinear constraints into linear ones, through properly introducing free auxiliary variables, i.e., it uses extra auxiliary variables in exchange for linearity.



[35] Z. Sun, Y. Wei, and K. Wu*

On energy laws and stability of Runge--Kutta methods for linear seminegative problems

SIAM Journal on Numerical Analysis,   60(5): 2448--2481, 2022.


[34] K. Wu 
Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics

SIAM Journal on Scientific Computing,   43(6): B1164--B1197, 2021.


[33] Z. Chen, V. Churchill, K. Wu, and D. Xiu
Deep neural network modeling of unknown partial differential equations in nodal space
Journal of Computational Physics,   449: 110782, 2022.


[32] K. Wu* and C.-W. Shu

Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations

Numerische Mathematik,  148: 699--741, 2021.


[31] Y. Chen and K. Wu*
A physical-constraint-preserving finite volume WENO method for special relativistic hydrodynamics on unstructured meshes
Journal of Computational Physics,   466: 111398, 2022.


[30] H. Jiang, H. Tang, and K. Wu*

Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields

Journal of Computational Physics,   463: 111297, 2022.


[29] K. Wu and Y. Xing

Uniformly high-order structure-preserving discontinuous Galerkin methods for Euler equations with gravitation: Positivity and well-balancedness

SIAM Journal on Scientific Computing,  43(1): A472--A510, 2021.


[28K. Wu and D. Xiu

Data-driven deep learning of partial differential equations in modal space

Journal of Computational Physics,  408: 109307, 2020. 


[27] K. Wu, T. Qin, and D. Xiu

Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data

SIAM Journal on Scientific Computing,  42(6): A3704--A3729, 2020. 


[26] K. Wu and C.-W. Shu

Entropy symmetrization and high-order accurate entropy stable numerical schemes for relativistic MHD equations

SIAM Journal on Scientific Computing,  42(4): A2230--A2261, 2020. 


[25] Z. Chen, K. Wu, and D. Xiu

Methods to recover unknown processes in partial differential equations using data

Journal of Scientific Computing,  85:23, 2020. 


[24] K. Wu, D. Xiu, and X. Zhong

A WENO-based stochastic Galerkin scheme for ideal MHD equations with random inputs 

Communications in Computational Physics,  30: 423--447, 2021.


[23] J. Hou, T. Qin, K. Wu and D. Xiu

A non-intrusive correction algorithm for classification problems with corrupted data

Commun. Appl. Math. Comput., 3: 337--356, 2021.


[22] K. Wu* and C.-W. Shu

Provably positive high-order schemes for ideal magnetohydrodynamics: Analysis on general meshes

Numerische Mathematik,  142(4): 995--1047, 2019.


[21] T. Qin, K. Wu, and D. Xiu

Data driven governing equations approximation using deep neural networks

Journal of Computational Physics,  395: 620--635, 2019.


[20] K. Wu and D. Xiu

Numerical aspects for approximating governing equations using data

Journal of Computational Physics,  384: 200--221, 2019.


[19] K. Wu and D. Xiu

Sequential approximation of functions in Sobolev spaces using random samples
Commun. Appl. Math. Comput.,  1: 449--466, 2019.


[18] K. Wu and C.-W. Shu

A provably positive discontinuous Galerkin method for multidimensional ideal magnetohydrodynamics

SIAM Journal on Scientific Computing,  40(5):B1302--B1329, 2018.


[17] K. Wu

Positivity-preserving analysis of numerical schemes for ideal magnetohydrodynamics
SIAM Journal on Numerical Analysis,  56(4):2124--2147, 2018.


[16] Y. Shin, K. Wu, and D. Xiu

Sequential function approximation with noisy data

Journal of Computational Physics,  371:363--381, 2018.


[15] K. Wu and D. Xiu

Sequential function approximation on arbitrarily distributed point sets

Journal of Computational Physics,  354:370--386, 2018.


[14] K. Wu and H. Tang

On physical-constraints-preserving schemes for special relativistic magnetohydrodynamics with a general equation of state

Z. Angew. Math. Phys.,  69:84(24pages), 2018.


[13] K. Wu and D. Xiu

An explicit neural network construction for piecewise constant function approximation

arXiv preprint arXiv:1808.07390, 2018.


[12] K. Wu, Y. Shin, and D. Xiu

A randomized tensor quadrature method for high dimensional polynomial approximation

SIAM Journal on Scientific Computing,  39(5):A1811--A1833, 2017. 


[11] K. Wu

Design of provably physical-constraint-preserving methods for general relativistic hydrodynamics

Physical Review D,  95, 103001, 2017. 


[10] K. Wu, H. Tang, and D. Xiu

A stochastic Galerkin method for first-order quasilinear hyperbolic systems with uncertainty

Journal of Computational Physics,  345:224--244, 2017. 


[9] K. Wu and H. Tang

Admissible states and physical-constraints-preserving schemes for relativistic magnetohydrodynamic equations

Math. Models Methods Appl. Sci. (M3AS),  27(10):1871--1928, 2017. 


[8] Y. Kuang, K. Wu, and H. Tang

Runge-Kutta discontinuous local evolution Galerkin methods for the shallow water equations on the cubed-sphere grid

Numer. Math. Theor. Meth. Appl.,  10(2):373--419, 2017. 


[7] K. Wu and H. Tang

Physical-constraint-preserving central discontinuous Galerkin methods for special relativistic hydrodynamics with a general equation of state

Astrophys. J. Suppl. Ser. (ApJS),  228(1):3(23pages), 2017. (2015 Impact Factor of ApJS: 11.257)


[6] K. Wu and H. Tang

A direct Eulerian GRP scheme for spherically symmetric general relativistic hydrodynamics

SIAM Journal on Scientific Computing,  38(3):B458--B489, 2016. 


[5] K. Wu and H. Tang

A Newton multigrid method for steady-state shallow water equations with topography and dry areas

Applied Mathematics and Mechanics,  37(11):1441--1466, 2016. 


[4] K. Wu and H. Tang

High-order accurate physical-constraints-preserving finite difference WENO schemes for special relativistic hydrodynamics

Journal of Computational Physics,  298:539--564, 2015.


[3] K. Wu, Z. Yang, and H. Tang

A third-order accurate direct Eulerian GRP scheme for one-dimensional relativistic hydrodynamics

East Asian J. Appl. Math.,  4(2):95--131, 2014.


[2] K. Wu and H. Tang

Finite volume local evolution Galerkin method for two-dimensional relativistic hydrodynamics

Journal of Computational Physics,  256:277--307, 2014. 


[1] K. Wu, Z. Yang, and H. Tang

A third-order accurate direct Eulerian GRP scheme for the Euler equations in gas dynamics

Journal of Computational Physics,  264:177--208, 2014.