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
Research Interests
◆ 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
◆ Approximation Theory and Uncertainty Quantification
Honors & Awards
◆ National Distinguished Young Scholar (2020)
◆ 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 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, PKU (2014–2016) (The biggest scholarship of PKU)
Selected Publications (see [Published Works] for Full Publications List)
◆ 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*
Geometric quasilinearization framework for analysis and design of bound-preserving schemes
submitted for publication, 2021. arXiv:2111.04722. 8 Nov 2021
◆ 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
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.
◆ Z. Sun, Y. Wei, and K. Wu*
On energy laws and stability of Runge--Kutta methods for linear seminegative problems
submitted to SIAM Journal on Numerical Analysis, 2022.
◆ 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.
◆ 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
◆ Y. Chen and K. Wu*
A physical-constraint-preserving finite volume method for special relativistic hydrodynamics on unstructured meshes
Journal of Computational Physics, submitted for publication, 2021.
◆ 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, submitted for publication, 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, 30: 423--447, 2021.
◆ 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.
◆ 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
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.
Professional Services
◆ Reviewer for AMS Mathematical Reviews
◆ Referee for scientific journals including
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