Associate Professor College of Science, Department of Mathematics

Employment:

• 2021.01-present: Associate Professor, Department of Mathematics, Southern University of Science and Technology

• 2016.08-2020.12: Postdoctoral Scholar, Department of Mathematics, The Ohio State University

• 2016.04-2016.08: Postdoctoral Fellow, Scientific Computing and Imaging Institute, University of Utah

Education:

• 2011-2016: Ph.D. School of Mathematical Sciences, Peking University

• 2007-2011: B.Sc. School of Mathematics and Statistics, Huazhong University of Science and Technology

Awards:

• Zhong Jiaqing Mathematics Award, the Chinese Mathematical Society (2019) One of the three major mathematics awards of the Chinese Mathematical Society (4 per 2 years)

• Outstanding Ph.D. Graduates Award, PKU (2016)

• Outstanding Youth Paper Award (First Prize), the China Society for Computational Mathematics (2015)

• First Prize of "Challenge Cup" May-4th Youth Science Award, PKU (2014)

• President Scholarship of PKU (2014–2016)

Personal Profile

Research

Machine Learning and Data-driven Modeling

Numerical Solutions of Partial Differential Equations

Computational Fluid Dynamics and Astrophysics

High-order Accurate Numerical Methods

Hyperbolic Conservation Laws

Structure-preserving Methods: Design and Analysis

Approximation Theory and Uncertainty Quantification


Publications Read More

Selected Publications:

31.  K. Wu, Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics, submitted, 2021.

30. Z. Chen, V. Churchill, K. Wu, and D. Xiu, Deep neural network modeling of unknown partial differential equations in nodal space, submitted, 2021.

29. K. Wu and C.-W. Shu, Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations, submitted, 2020.

    Numerische Mathematik,   accepted for publication, 2021.

28. 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, A472–A510, 2021.

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

    Journal of Computational Physics, 408: 109307, 2020.

24. 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.

23. K. Wu, D. Xiu, and X. Zhong, A WENO-based stochastic Galerkin scheme for ideal MHD equations with random inputs

    Communications in Computational Physics, accepted for publication, 2020.

22. J. Hou, T. Qin, K. Wu and D. Xiu, A non-intrusive correction algorithm for classification problems with corrupted data

    Commun. Appl. Math. Comput., in press, 2020.

21. 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.

20. T. Qin, K. Wu, and D. Xiu, Data driven governing equations approximation using deep neural networks

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

19. K. Wu and D. Xiu, Numerical aspects for approximating governing equations using data

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

18. K. Wu and D. Xiu, Sequential approximation of functions in Sobolev spaces using random samples

    Commun. Appl. Math. Comput., 1: 449–466, 2019.

17. 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.

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, Positivity-preserving analysis of numerical schemes for ideal magnetohydrodynamics

    SIAM Journal on Numerical Analysis, 56(4):2124–2147, 2018.

14. K. Wu and D. Xiu, Sequential function approximation on arbitrarily distributed point sets

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

13. 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.

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.

Lab members Read More

Join us

2 postdoc positions available

The candidates should have a Ph.D. degree in Mathematics, Computational Physics, Fluid Mechanics, or Computer Science. Research experience in numerical PDE, CFD, machine learning, and/or data science is desirable. Salary package is competitive. If you are interested, please send your CV to WUKL@sustech.edu.cn

 

课题组正在招聘博士后1-2名;计划招收博士生1名、硕士生1名。

有意者请将相关应聘材料发送至:WUKL@sustech.edu.cn。邮件主题为“应聘岗位-应聘人姓名”(如,应聘博士后-张三)。

博士后招聘

博士后申请人应具有博士学位和学历,品学兼优、身心健康、年龄不超过35 岁;获得博士学位不超过3 年;能够保证全职在站从事博士后研究工作。

岗位要求

1) 数学、计算物理、流体力学、计算机或其他相关专业,已获得或即将获得博士学位;

2) 有计算数学、计算流体力学(特别是可压缩流体)、机器学习或数据科学的研究经验者优先;

3) 具有良好的数学基础,精通至少一种计算机编程语言(C/C++、Python 或FORTRAN);

4) 具有良好的英文阅读、写作和交流能力;

5) 博士期间至少发表过1 篇高水平学术论文;

6) 对科学研究有非常浓厚的兴趣,敢于探索挑战性的科学问题;

7) 有上进心、独立思考精神、工作勤奋踏实、具有良好的团队合作精神。

工作待遇和福利

1) 博士后聘用期两年,年薪33万元起(含广东省生活补助15万元及深圳市生活补助6万元),并按深圳市有关规定参加社会保险及住房公积金;博士后福利费参照学校教职工标准发放。

2) 特别优秀候选人可以申请校长卓越博士后,年薪可达50万元以上(含广东省及深圳市补助)。

3) 在站期间,可依托学校申请深圳市公租房,未依托学校使用深圳市公租房的博士后,可享受两年税前2800元/月的住房补贴。

4) 拥有优良的工作环境和境内外合作交流机会,博士后在站期间享受两年共计2.5万学术交流经费资助。

5) 课题组协助符合条件的博士后申请“广东省海外青年博士后引进项目”。即在世界排名前200名的高校(不含境内,排名以上一年度泰晤士、USNEWS、QS和上海交通大学的世界大学排行榜为准)获得博士学位,在广东省博士后设站单位从事博士后研究,并承诺在站2年以上的博士后,申请成功后省财政给予每名进站博士后资助60万元生活补贴(与广东省每年15万生活补助不同时享受,与深圳市每年6万元生活补助同时享受情况以深圳市规定为准);对获得本项目资助,出站后与广东省用人单位签订工作协议或劳动合同,并承诺连续在粤工作3年以上的博士后,省财政给予每人40万元住房补贴。

6) 博士后出站选择留深从事科研工作,且与本市企事业单位签订3年以上劳动(聘用)合同的,可以申请深圳市博士后留深来深科研资助。深圳市政府给予每人每年10万元科研资助,共资助3年(以深圳市最新申报要求为准)。

7) 博士后进站后可申请深圳市户口,对于符合最新《深圳市新引进人才租房和生活补贴》相关政策要求的博士后,落户深圳后,可协助申请深圳市一次性租房和生活补贴3万元(免税,自主网上申请)。

8) 依据自身符合的条件情况,在站或出站留深博士后可申请 "深圳市孔雀计划C类人才"或者"深圳市后备级人才",享受5年160万的奖励津贴(免税)(以深圳市最新相关人才申报要求为准)。

应聘材料

1) 个人详细简历,包括出生年月、联系方式、预计到岗时间、从本科起教育背景、工作经历等;

2) 能充分反映本人学术水平的有关材料,例如,学术成果总结、已发表论著列表、代表性论著全文、成果获奖情况等;

3) 提供至少2名推荐人的姓名及其有效联系方式。

研究助理招聘

岗位职责:协助课题组的科研工作。

岗位要求

1) 拥有计算数学或相关专业本科或硕士学位,学习成绩优异;

2) 有计算数学、计算流体力学、机器学习或数据科学的研究经验者优先;

3) 具有良好的数学基础,精通至少一种计算机编程语言(C/C++、Python、FORTRAN 或MATLAB);

4) 具有良好的英文阅读和写作能力;

5) 对科学研究有浓厚的兴趣,工作勤奋踏实,具有良好的团队合作精神;

6) 有志于在计算数学方向进一步深造(攻读博士学位)的申请人优先考虑。

工作待遇:年薪6–10万元,根据工作表现可补充额外绩效奖励;具体待遇面议。

应聘材料

1) 个人详细简历,包括出生年月、联系方式、预计到岗时间、从本科起教育背景、工作经历等;

2) 本科或研究生阶段的成绩单;

3) 能充分反映本人学术水平的有关材料,例如,学术成果总结、已发表论著列表、代表性论著全文、成果获奖情况等。

南科大研究生招生简介

学制:硕士 2 年;非硕士起点博士 5 年,硕士起点博士 4 年;境外联培博士 4 年。

住宿:目前硕士为两人间宿舍,博士单人间宿舍,住宿费 1300元/ 年,住宿条件在全国高校领先。

学校将为研究生提供充足的资助(目前博士生最高 8+2 万/年, 硕士生最高 4+1 万/年),以及学术交流(包括海外交流)的机会。

与境外大学联合培养的博士生有约 2 年的时间在境外学习,享受对方博士生同等待遇,由双方教授共同指导,发对方学校的学位证书。

更多详情请见南方科技大学研究生院官网:http://gs.sustech.edu.cn

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Contact Us

Contact Address

广东省深圳市南山区学苑大道1088号慧园3栋525

Office Phone

Email

WUKL@sustech.edu.cn

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