師資
吳開亮，男，籍貫安徽省安慶市，理學博士，南方科技大學數學系副教授、博士生導師。2011年獲華中科技大學數學學士學位；2016年獲北京大學計算數學博士學位；2016-2020年先后在美國猶他大學和美國俄亥俄州立大學從事博士后研究工作；2021年1月加入南方科技大學、任副教授。研究方向包括計算流體力學與數值相對論、機器學習與數據驅動建模、微分方程數值解、高維逼近與不確定性量化等。研究成果發表在SINUM，SISC，Numer. Math.，M3AS，J. Comput. Phys.，JSC，ApJS，Phys. Rev. D等期刊上。曾獲中國數學會計算數學分會 優秀青年論文獎一等獎（2015）和中國數學會 鐘家慶數學獎（2019）。
研究領域
微分方程數值解、計算流體力學與數值相對論、機器學習與數據科學、計算物理、高維逼近論與不確定性量化
榮譽及獲獎
◆ 2019：中國數學會 鐘家慶數學獎
◆ 2016：北京大學 優秀畢業生
◆ 2015：中國數學會計算數學分會 優秀青年論文獎一等獎
◆ 2014：北京大學 “挑戰杯”五四青年科學獎一等獎
代表性論文 （更新于2021年5月）
◆ 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
Provably positive high-order schemes for ideal magnetohydrodynamics: Analysis on general meshes
Numerische Mathematik, 142(4): 995--1047, 2019.
◆ 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 and C.-W. Shu
Provably physical-constraint-preserving discontinuous Galerkin methods for multidimensional relativistic MHD equations
Numerische Mathematik, accepted for publication, 2021.
◆ K. Wu
Minimum principle on specific entropy and high-order accurate invariant region preserving numerical methods for relativistic hydrodynamics
submitted for publication, arXiv:2102.03801, 2021.
◆ 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, submitted for publication, 2021.
◆ 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.
◆ 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.
◆ 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, 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.
◆ 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.
◆ T. Qin, K. Wu, and D. Xiu
Data driven governing equations approximation using deep neural networks
Journal of Computational Physics, 395: 620--635, 2019.
◆ K. Wu and D. Xiu
Numerical aspects for approximating governing equations using data
Journal of Computational Physics, 384: 200--221, 2019.
◆ 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.
◆ Y. Shin, K. Wu, and D. Xiu
Sequential function approximation with noisy data
Journal of Computational Physics, 371:363--381, 2018.
◆ 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, 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
Design of provably physical-constraint-preserving methods for general relativistic hydrodynamics
Physical Review D, 95, 103001, 2017.
◆ 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
Admissible states and physical-constraints-preserving schemes for relativistic magnetohydrodynamic equations
Math. Models Methods Appl. Sci. (M3AS), 27(10):1871--1928, 2017.
◆ 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.
◆ 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)
◆ 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.
◆ 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.
◆ 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.
◆ 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.
學術服務
◆ 美國《數學評論》評論員
◆ 下列期刊審稿人
Communications in Computational Physics
Computer Methods in Applied Mechanics and Engineering
East Asian Journal on Applied Mathematics
Engineering Optimization
Journal of Computational and Applied Mathematics
Journal of Computational Physics
Journal of Scientific Computing
Journal of Applied Mathematics and Computing
Mathematical Models and Methods in Applied Sciences (M3AS)
Mathematica Numerica Sinica
SIAM Journal on Scientific Computing
SIAM/ASA Journal on Uncertainty Quantification