Prof. Dr. rer. nat. Mike Espig

Professur für Mathematik

+49 375 536 1381 / 1388
+49 375 536 1390
Zimmer: R II 365 (Dr.-Friedrichs-Ring 2A, Haus R II)
mike.espig[at]fh-zwickau.de

Sprechzeiten

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Anschrift

Westsächsische Hochschule Zwickau
Fakultät Physikalische Technik/Informatik
Fachgruppe Mathematik
PSF 201037
D 08012 Zwickau

 

Lehr- und Forschungsgebiete

Mathematik für Ingenieure
Analysis und Numerik
Numerik hochdimensionaler Probleme mittels Tensorformate

Akademischer Werdegang

seit 3/2017
Professor für Mathematik, Westsächsischen Hochschule Zwickau, Fakultät Physikalische Technik/
Informatik

4/2014-2/2017
Professor für Numerische und Angewandte Analysis, Rheinisch-Westfälische
Technische Hochschule Aachen (RWTH Aachen), Institut für Geometrie und Praktische Mathematik

10/2013– 3/2014
Wissenschaftlicher Mitarbeiter, Technische Universität Berlin, Institut für Mathematik,
Numerische Analysis partieller Differentialgleichungen, Prof. Dr. Harry Yserentant.

1/2008–9/2013
Wissenschaftlicher Mitarbeiter, Max-Planck-Institut für Mathematik in den
Naturwissenschaften, Leipzig, Wissenschaftliches Rechnen, Prof. Dr. Dr. h.c. W. Hackbusch.

04/2005–12/2007
Promotion zum Dr. rer. nat. im Fachgebiet Mathematik, Max-Planck-Institut
für Mathematik in den Naturwissenschaften, Effiziente Bestapproximation mittels
Summen von Elementartensoren in hohen Dimensionen, Betreuer: Prof. Dr. Dr. h.c. W. Hackbusch.

1999–2004
Studium der Mathematik und Physik im Nebenfach, Dipl.-Math., Christian-
Albrechts-Universität, Kiel, Approximation von Resolventen des Laplace-Operators
mit Hilfe hierarchischer Matrizen, Betreuer: Prof. Dr. Dr. h.c. W. Hackbusch.

    Ausgewählte Vorträge

    2018 Anaheim, SIAM Conference on Uncertainty Quantification (UQ18), Low-Rank Tensors for
    Stochastic Forward Problems

    2017 WIAS, Berlin, Workshop on Mathematics of Deep Learning 2017, An Effcient Method for Statistical Learning by Means of Tensor Format
    Representations

    2016 BIRS, Banff, The Numerical Treatment of High Dimensional Problems by Means of Tensor Format Representations

    2016 San Servolo, Venice, The Convergence of Alternating Steepest Descent Optimisation in Tensor Format Representations

    2015 SIAM CSE, Salt Lake City, Tensor Format Representations and Optimal Model
    Reduction for Uncertainty Quantification.

    2014 TU Berlin, The Treatment of High Dimensional Problems by Means of Tensor Format
    Representations.

    2014 Oberwolfach, On the Convergence of Alternating Least Squares Optimisation in
    Tensor Format Representations.

    2013 Oberwolfach, The Numerical Treatment of Partial Differential Equations with Stochastic
    Coefficients by Means of Tensor Format Representation.

    2013 HDA 2013, ANU Canberra, Alternating Least Squares Optimisation in Tensor
    Format Representations.

    2012 GAMM-Jahrestagung, Darmstadt, On the Convergence of Alternating Least
    Squares Optimisation in Tensor Format Representations.

    2011 GAMM-Jahrestagung, Graz, Treatment of High Dimensional Problems by Means
    of Tensor Networks.

    2011 HIM, Bonn, Tensor Networks.

    2010 ILAS, Pisa, Optimisation Problems in Tensor Networks.

    2009 GAMM-Jahrestagung, Gdansk, On the Efficient Treatment of High Dimensional
    Problems by Means of Elementary Tensor Sums.

    2009 USC, Los Angeles, Efficient Treatment of High Dimensional Problems.

    2008 SIAM-Jahrestagung, San Diego, Efficient Best-Approximation of Tensor-Sums
    and Applications in High Dimensions.

    2007 ENUMATH, Graz, Approximation of Tensor-Sums in High Dimensions with Application
    to Multi-Dimensional Operators.

    2007 Nonlinear and Adaptive Approximation in High Dimensions, Bad Honnef,
    Efficient Best-Approximation of Tensor-Sums and Applications.

    Zur Veröffentlichung eingereichte Artikel

    The Alternating Steepest Descent Method for Solving Linear Systems in Tensor Format Representations, M. Espig.

    An Efficient Method for Statistical Learning by Means of Tensor Format Representations, M. Espig.

    Ausgewählte Artikel

    S.R. Chinnamsetty, M. Espig, B.N. Khoromskij, W. Hackbusch, H.J. Flad, et al. Tensor
    product approximation with optimal rank in quantum chemistry. J. Chem. Phys,
    127(8):84110–84110, 2007.

    M. Espig and W. Hackbusch. On the robustness of elliptic resolvents computed by
    means of the technique of hierarchical matrices. Appl. Numer. Math., 58(12):1844–1851,
    December 2008.

    M. Espig, L. Grasedyck, and W. Hackbusch. Black box low tensor-rank approximation
    using fiber-crosses. Constructive approximation, 30(3):557–597, 2009.

    S.R. Chinnamsetty, M. Espig, H.J. Flad, and W. Hackbusch. Canonical tensor products
    as a generalization of gaussian-type orbitals. Journal of Research in Physical Chemistry
    and Chemical Physics, 224(3-4):681–694, 2010.

    U. Benedikt, A.A. Auer, M. Espig, and W. Hackbusch. Tensor decomposition in posthartree-
    fock methods. i. two-electron integrals and mp2. J. Chem. Phys, 134(5):4118,
    2011.

    Mike Espig, Wolfgang Hackbusch, Stefan Handschuh, and Reinhold Schneider. Optimization
    problems in contracted tensor networks. Computing and Visualization in
    Science, 14:271–285, 2011.

    Mike Espig, Wolfgang Hackbusch, Thorsten Rohwedder, and Reinhold Schneider.
    Variational calculus with sums of elementary tensors of fixed rank. Numerische
    Mathematik, 122:469–488, 2012.

    Mike Espig and Wolfgang Hackbusch. A regularized newton method for the efficient
    approximation of tensors represented in the canonical tensor format. Numerische
    Mathematik, 122:489–525, 2012.

    Mike Espig, Wolfgang Hackbusch, Alexander Litvinenko, Hermann G. Matthies, and
    Elmar Zander. Efficient analysis of high dimensional data in tensor formats. In Jochen
    Garcke and Michael Griebel et al., editors, Sparse Grids and Applications, volume 88
    of Lecture Notes in Computational Science and Engineering, pages 31–56. Springer
    Berlin Heidelberg, 2013.

    Mike Espig, Wolfgang Hackbusch, Alexander Litvinenko, Hermann G. Matthies, and
    Philipp Wähnert. Efficient low-rank approximation of the stochastic galerkin matrix
    in tensor formats. Computers and Mathematics with Applications, November 2012.

    Udo Benedikt, Henry Auer, Mike Espig, Wolfgang Hackbusch, and Alexander A. Auer.
    Tensor representation techniques in post-Hartree-Fock methods : matrix product state
    tensor format. Molecular Physics, 111(16/17):2398–2413, 2013.

    Sambasiva Rao Chinnamsetty, Mike Espig, and Wolfgang Hackbusch. Mesh-free
    canonical tensor products for six-dimensional density matrix : Computation of Kinetic
    Energy in Electronic Structure Calculations. 2013.

    K. H. Böhm, A.A. Auer, and M. Espig. Tensor representation techniques for full
    configuration interaction: A fock space approach using the canonical product format.
    J. Chem. Phys, 144, 2016.