Zhu Wang



Education
Ph.D.  Mathematics  Virginia Tech  2012 
M.S.  Computational Mathematics  Sichuan University, China  2006 
B.A.  Information & Computational Mathematics  Sichuan University, China  2003 
Experience
2014 – Present  Assistant Professor  Department of Mathematics, Univ. of South Carolina 
2012 – 2014  Industrial Postdoc  IMA, Univ. of Minnesota 
2011  Givens Associate  Argonne Natinal Laboratory 
2010  Givens Associate  Argonne National Laboratory 
Research
Research Interests
My research centers around the development of mathematically justified models and corresponding efficient, accurate algorithms for grand challenge problems at the frontier of computational science and engineering. Topics include: Scientific Computing, Numerical Analysis, ReducedOrder Modeling, Climate Modeling, Large Eddy Simulation, Numerical Solutions to PDEs.
Current Projects
 Reducedorder modeling for complex systems  Reducedorder modeling is a powerful technique to decrease the tremendous computational cost required in many realworld problems, e.g., the control of turbulent flows. The proper orthogonal decomposition (POD) combined with Galerkin method has been widely used to generate reducedorder models (ROMs) for flows. However, this methodology breaks down when the complexity of the flow increases. To address the lack of physical accuracy of standard PODROMs, novel POD closure models were introduced for structurally dominated turbulent flows. The new models have been applied to the airflow control in energy efficient buildings and uncertainty analysis in nuclear engineering.
 Variational approaches to inverse photolithograph  Optical lithography is a typical process utilized in producing microchips, which transfers a layout pattern from a photomask to a substrate, under an ultraviolet light source. Along with the growing demand for circuit components with smaller and smaller scales, the classic manufacturing method is not able to resolve the fine details of the circuit components. As a result, the pattern on the substrate may have wrong spots connected. We develop a variational approach to design an appropriate photomask such that the final pattern after a complete lithography process remains as close as possible to the target pattern.
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Teaching Activities
Current Courses
 MATH 344: Applied Linear Algebra
Previous Courses
 MATH 141: Calculus I
 MATH 142: Calculus II
 MATH 241: Vector Calculus
 MATH 242: Differential Equations
 MATH 344: Applied Linear Algebra
 MATH 520: Differential Equations
 MATH 720: Applied Mathematics I
 MATH 721: Applied Mathematics II
 Courses taught while at Va Tech: Calculus, Elementary Calculus with Trig II, Vector Geometry Recitation.
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Honors and Other Special Scientific Recognition
 SIAM CSE 3rd BGCE Student Paper Prize Finalist, Reno, NV, 2011
 Winner of the 34th SIAM SEAS Conference Student Paper Competition, Raleigh, NC, 2010
 C. B. Ling Scholarship, Virginia Tech, 20082009
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5 Selected Publications
 L. Ju and Z. Wang. Exponential Time Differencing Gauge Method for Incompressible Viscous Flows, Comm. Comp. Phys., vol. 22, 2017, pp. 517541
 D. Wells, X. Xie, Z. Wang and T. Iliescu. An EvolveThenFilter Regularized Reduced Order Model For ConvectionDominated Flows, Int. J. Numer. Meth. Fluids, vol. 84, 2017, pp. 598615
 Y. Gong, Q. Wang and Z. Wang. StructurePreserving Galerkin POD ReducedOrder Modeling of Hamiltonian Systems, Comput. Meth. Appl. Mech. Eng., vol. 315, 2017, pp. 780798
 X. Xie, D. Wells, Z. Wang, and T. Iliescu. Approximate Deconvolution Reduced Order Modeling, Comput. Meth. Appl. Mech. Eng., vol. 313, 2017, pp. 512534
 J. Borggaard, Z. Wang and L. Zietsman. A GoalOriented Model Reduction Approach for Complex Systems, Comput. Math. Appl. 71 (11), 2016, pp. 21552169
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