Left: Me; Right: Gilbert Strang; At SIAM AN18, Portland OR

Simon, Sai-Mang Pun

Blocker 608N

Department of Mathematics, TAMU

College Station TX, 77843

Email: smpun@math.tamu.edu

Curriculum Vitae: Click here

MathSciNet: Click here

Math Genealogy Project: Click here

Google Scholar: Click here

Quotes

One can know everything about a matrix if one knows the spectrum of this matrix.
Rewriting the equation does not critically change the solution, but it can help our understanding.
A great discovery solves a great problem but there is a grain of disvoery in the solution of any problem.
Young man, in mathematics you don't understand things. You just get used to them. - John von Neumann
Happy wife, happy life!

Current Teaching

At TAMU:

2020 Fall:

MATH251-520 Engineering Mathematics III.

Research interests

My current research interests include numerical analysis for partial differential equations with multiple scales.
The main technique for solving these problems is generalized multiscale finite element method (GMsFEM).
I am also interested in studying the goal-oriented adaptivity within the framework of GMsFEM.

Publications

E. Chung, S. Pollock and S.-M. Pun. Goal-oriented adaptivity of mixed GMsFEM for flows in heterogeneous media. Computer Methods in Applied Mechanics and Engineering, 323 (2017), pp.151-173. Download
E. Chung, S.-M. Pun and Z. Zhang. An adaptive dynamically low-dimensional approximation method for multiscale stochastic diffusion equations. Journal of Computational and Applied Mathematics, 393 (2019), pp.302-313. Download
R. Altmann, E. Chung, R. Maier, D. Peterseim and S.-M. Pun. Computational multiscale methods for linear heterogeneous poroelasticity. Journal of Computational Mathematics 38 (1) 2020, 41-57.
E. Chung, S. Pollock and S.-M. Pun. Online basis construction for goal-oriented adaptivity in the GMsFEM. Journal of Computational Physics, 393 (2019), pp.59-73. Download
S. Fu, R. Altmann, E. Chung, R. Maier, D. Peterseim and S.-M. Pun. Computational multiscale methods for linear heterogeneous poroelasticity with high contrast. Journal of Computational Physics, 395 (2019), pp. 286-297. Download
E. Chung and S.-M. Pun. Online adaptive basis enrichment for mixed CEM-GMsFEM. Multiscale Modeling & Simulation 17 (4) 2019, 1103-1122.
E. Chung and S.-M. Pun. Computational multiscale methods for first-order wave equation using CEM-GMsFEM. Journal of Computational Physics 409, 2020.
E. Chung, J. Hu, and S.-M. Pun. Convergence of the CEM-GMsFEM for Stokes flows in heterogeneous perforated domains. arXiv preprint arXiv.2007.03237, 2020.
B. Chetverushkin, E. Chung, Y. Efendiev, S.-M. Pun, and Z.-C. Zhang. Computational multiscale methods for quasi-gas dynamic equations. arXiv preprint arXiv.2009.00068, 2020.
E. Chung, W.-T. Leung, S.-M. Pun, and Z.-C. Zhang. A multi-stage deep learning based algorithm for multiscale model reduction. arXiv preprint arXiv.2009.11341, 2020.
Y. Efendiev, S.-M. Pun, and and P. N. Vabishchevich. Temporal splitting algorithms for non-stationary multiscale problems. arXiv preprint arXiv:2011.06077, 2020.
E. Chung, Y. Efendiev, W.-T. Leung, S.-M. Pun, and Z.-C. Zhang. Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem. arXiv preprint arXiv.2011.08954, 2020.

Past Teaching

At TAMU:

2020 Spring:	Course Instructor, MATH417-503/504 Numerical Methods.
2019 Fall:	Course Instructor, MATH251-504 Engineering Mathematics III.

At CUHK:

2019 Spring:	Teaching assistant, MATH2221 Mathematics Laboratory II.
2018 Fall:	Teaching assistant, MATH3290 Mathematical Modeling.
	MATLAB review guide;
	Additional example for Chapter 5.
2018 Spring:	Teaching assistant, MATH2221 Mathematics Laboratory II.
2017 Fall:	Teaching assistant, MATH3290 Mathematical Modeling.
2017 Spring:	Teaching assistant, MATH2230 Complex Variables and applications.
2016 Fall:	Teaching assistant, MATH3290 Mathematical Modeling.
2015 Fall:	Teaching assistant, MATH3290 Mathematical Modeling;
	Teaching assistant, MATH2230 Complex Variables and applications.

Links