Dr. Guangning Tan (谭广宁)

PhD of Computational Science and Engineering

School of Computational Science and Engineering (CSE)

McMaster University

Hamilton, Ontario, L8S 4K1, CANADA

Supervisor: Professor Ned Nedialkov


Personal website: tgn3000.com

Curriculum Vitae: CV in English    中文简历

Publications: PDF BibTex

Teaching statement:  PDF Cover letter:  PDF

ORCID page:  orcid.org/0000-0001-7983-2691

Research interest: numerical methods for ODEs/DAEs, Matlab programming, automatic differentiation, numerical analysis

Research focus: Structural Analysis of Differential-Algebraic Equations

研究兴趣:微分代数方程,结构分析(Sigma方法),三角分块,自动微分,并行计算,GPU编程,常/偏微分方程数值解法,科学计算与数值分析

Teaching

  • Mailbox for teaching: tgnteach@gmail.com
  • Matlab fundamentals
  • TA materials for SE4X03 (Scientific computing)
  • TA materials for SE3MX3 (Signals and Systems)
  • TA materials for SE3F03 (Machine-Level Computer Programming) Lecture notes

    Education

  • Doctor of Philosophy in Computational Science and Engineering (CSE)

    McMaster University, Hamilton, Ontario, Canada. 2012.1--2016.8

    Thesis: Conversion methods for improving structural analysis of DAEs

    Manuscript PDF Defense Slides PDF

    Master in CSE, McMaster University, 2010.9--2011.12

    Bachelor in Communication and Electrical Engineering

    School of Information Science and Technology, Sun Yat-sen University (中山大学)

    Guangzhou, China, 2006.9--2010.6

    High School: Affiliated High School of South China Normal University (华南师范大学附属中学, HSFZ)

    Guangzhou, China, 2000.9--2006.6

    Software

    DAESA---Differential-Algebraic Equations Structural Analyzer in MATLAB Download

    Authors: Guangning Tan, Nedialko S. Nedialkov, John D. Pryce, Ross McKenzie

    Online introduction and tutorial of DAESA

    Selected talks

  • Computing derivatives of a DAE solution in parallel. The best presentation in the 2016 CSE Student Symposium, McMaster

  • Conversion methods for improving structural analysis of DAEs. Invited seminar talk at Department of Computer Science, University of Toronto. April 2016

  • Conversion methods for improving structural analysis of DAEs. Interview seminar talk at PSEL, MIT. April 2016

  • Symbolic-numeric methods for improving structural analysis of DAEs. SONAD-ACMES conference. Western University, London, Ontario, Canada. 2015. Slides. Recording on Youtube.

    Published Articles

  • J. D. Pryce, N. S. Nedialkov, and G. Tan, DAESA: a Matlab tool for structural analysis of differential-algebraic equations: Theory, ACM Trans. Math. Softw., 41 (2015), pp. 9:1-9:20.  PDF
  • N. S. Nedialkov, J. D. Pryce, G. Tan. Algorithm 948: DAESA: a Matlab tool for structural analysis of differential-algebraic equations: Software, ACM Trans. Math. Softw., 41 (2015), pp. 12:1-12:14.  PDF
  • Book Chapters and Technical Reports

  • Conversion methods for improving structural analysis of differential-algebraic equation systems. Submitted to BIT Numerical Mathematics, 2016   arXiv:1608.06691
  • Conversion methods, block triangularization, and structural analysis of differential-algebraic equation systems. Submitted to BIT Numerical Mathematics, 2016  arXiv:1608.06693
  • G. Tan, N. S. Nedialkov, J. D. Pryce. Symbolic-numeric methods for improving structural analysis of DAEs. Mathematical and Computational Approaches in Advancing Modern Science and Engineering, pp. 763-773. Springer International Publishing, Cham (2016)  DOI 10.1007/978-3-319-30379-6 68
  • G. Tan, N. S. Nedialkov, J. D. Pryce. Symbolic-numeric methods for improving structural analysis of DAEs.  Technical report CAS-15-07-NN, Dept. of Computing and Software, McMaster University, 2015.  PDF
  • N. S. Nedialkov, G. Tan, J. D. Pryce. Exploiting fine block triangularization and quasilinearity in DAEs. Technical report CAS-14-08-NN, 2014.  PDF
  • J. D. Pryce, N. S. Nedialkov, G. Tan. Graph theory, irreducibility, and structural analysis of DAEs. Technical report CAS-14-09-NN, 2014  PDF
  • G. Tan, N. S. Nedialkov, J. D. Pryce. A simple method for quasilinearity analysis of DAEs. Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science, pp. 445--450. Springer International Publishing, Cham (2015)  DOI 10.1007/978-3-319-12307-3-64
  • J. D. Pryce, N. S. Nedialkov, G. Tan, R. McKenzie. Exploiting block triangular form for solving DAEs: reducing the number of initial values. Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science, pp. 367--373. Springer International Publishing, Cham (2015)  DOI 10.1007/978-3-319-12307-3-53

  • To my parents



    Copyright 2016. Guangning Tan. Last update: 14 November, 2016