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 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, 2022. 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 DG schemes via geometric quasilinearization for ideal MHD equations, SIAM Journal on Numerical Analysis, 2022. 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, 202 0 . 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.
S. Cui, S. Ding, and K. Wu*, On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws, preprint, 2022. Y. Ren, K. Wu, J. Qiu, and Y. Xing, On positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation, preprint, 2022.
S. Ding and K. Wu*, A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations, preprint, 2023. 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. J. Chen and K. Wu*, Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems, preprint, 2022.
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.
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. 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. K. Wu, D. Xiu, and X. Zhong , A WENO-based stochastic Galerkin scheme for ideal MHD equations with random inputs , Communications in Computational Physics, 202 1 . 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） 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） ◆  Dr. Shumo CUI (2023.2.1-) 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" submitted to SINUM. Postdoctoral Fellows
◆  Dr. Shengrong DING (2021.11-present):  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 " submitted to SINUM. We have a joint article "A new discretely divergence-free positivity-preserving high-order finite volume method for ideal MHD equations" submitted to SISC. ◆  Dr. Junfeng CHEN (Postdoctoral Fellow: 2022.10-present; Visiting Postdoc Scholar: 2022.03-2022.09):  B.Sc. from Tsinghua University（清华本科），Ph.D. from Paris Sciences et Lettres – PSL Research University（法国巴黎文理研究大学博士）. We have a joint article "Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems" submitted to JCP. Graduate Students ◆  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" accepted for publication in《SIAM Journal on Numerical Analysis》. We have a joint article with Prof. Huazhong Tang: " Positivity-preserving well-balanced central discontinuous Galekin schemes for the Euler equations under gravitational fields " published in《Journal of Computational Physics》. ◆  Fang YAN (2021.09-)，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）.
◆  Linfeng XU (2022.09-)，Master Student，B.Sc. from SUSTech（南科大）. Undergraduate Students ◆  Xinran FANG ◆  Yunhao JIANG：He was selected into a joint study program in University of Wisconsin-Madison.  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. 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 ◆  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 ◆  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
◆  Yuanji ZHONG

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 On optimal cell average decomposition for high-order bound-preserving schemes of hyperbolic conservation laws, submitted, 2023.
[42] J. Chen and K. Wu* Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems, submitted, 2023. On positivity-preserving well-balanced finite volume methods for the Euler equations with gravitation Journal of Computational Physics ,  submitted, 2022. A new locally divergence-free path-conservative central-upwind scheme for ideal and shallow water magnetohydrodynamics, SIAM Journal on Scientific Computing , submitted, 2022.
[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 ,  accepted, 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. Provably positive central DG schemes via geometric quasilinearization for ideal MHD equations
SIAM Journal on Numerical Analysis ,   accepted, 2022. [35] K. Wu and C.-W. Shu*
Geometric quasilinearization framework for analysis and design of bound-preserving schemes
SIAM Review , accepted,  2022.  arXiv:2111.04722.  8 Nov 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.
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.
Positivity-preserving well-balanced central discontinuous Galerkin schemes for the Euler equations under gravitational fields Journal of Computational Physics, 463: 111297, 2022. 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. Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data SIAM Journal on Scientific Computing ,  42(6): A3704--A3729, 2020. 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. 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. A provably positive discontinuous Galerkin method for multidimensional ideal magnetohydrodynamics SIAM Journal on Scientific Computing ,  40(5):B1302--B1329, 2018. On physical-constraints-preserving schemes for special relativistic magnetohydrodynamics with a general equation of state Z. Angew. Math. Phys. ,  69:84(24pages), 2018. 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. A third-order accurate direct Eulerian GRP scheme for one-dimensional relativistic hydrodynamics East Asian J. Appl. Math. ,  4(2):95--131, 2014. 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.