University of Wisconsin-Madison Wisconsin Institute for Discovery | Mechanical Engineering
Benjamin Peherstorfer
Assistant Professor

Department of Mechanical Engineering
Wisconsin Institute for Discovery (affiliated)
Grainger Institute (affiliated)
University of Wisconsin-Madison

A0407-1513 University Ave
Madison, WI 53706

E-mail: peherstorfer at
Office: 2254 ME
Phone: (608) 265-6781
      University of Wisconsin-Madison

Research interests

  • Computational statistics, scientific computing
  • Bayesian inference, uncertainty propagation
  • Multifidelity methods, model reduction
  • Machine learning, statistical learning
  • High-dimensional problems, sparse grids

Short CV

09/2016 - present:   Assistant Professor
University of Wisconsin-Madison
01/2014 - 08/2016:   Postdoctoral Associate
Willcox group, Massachusetts Institute of Technology
09/2010 - 09/2013:   PhD
Scientific Computing, Technical University of Munich


Jul 2017:   Invited to speak at the workshop Quantification of Uncertainty: Improving Efficiency and Technology that is organized by Marta D'Elia (Sandia), Max Gunzburger (Florida State), and Gianluigi Rozza (SISSA)
Apr 2017:   Presentation in the colloquium of the Department of Mathematics at Virginia Tech
Mar 2017:   Invited presentation at the workshop Uncertainty Quantification and Data-Driven Modeling that is organized by James R. Stewart (Sandia) and Krishna Garikipati (UMich)
Feb 2017:   Co-organizer of minisymposium on surrogate modeling at SIAM Computational Science and Engineering 2017; with Gianluigi Rozza (SISSA)
Jan 2017:   I became an affiliate of the Wisconsin Institute for Discovery/Optimization
Oct 2016:   Presentation in the Applied and Computational Mathematics Seminar at University of Wisconsin-Madison
Aug 2016:   I started as Assistant Professor at University of Wisconsin-Madison.
Aug 2016:   Invited presentation at workshop on Next Generation Mobility Modeling and Simulation, UW-Madison
Jul 2016:   Co-organizer of minisymposium on model reduction at SIAM Annual Meeting 2016
Mar 2016:   Co-organizer of the workshop on data-driven model reduction and machine learning
Nov 2015:   Invited talk in the seminar series of the Transregional Collaborative Research Center on Invasive Computing
Mar 2015:   Co-organizer of minisymposium on adaptive model reduction at SIAM CSE 15
Dec 2014:   I was awarded the Heinz Schwärtzel prize for my PhD thesis
Dec 2014:   Invited talk in the scientific computing colloquium at TUM
Apr 2014:   Co-organizer of minisymposium on density estimation at SIAM UQ 14
Jan 2014:   Started as Postdoctoral Associate in the group of Karen Willcox at MIT.
Sep 2013:   Co-organizer of the workshop on adapt./local. MOR with machine learning

Selected preprints

[1] Peherstorfer, B., Kramer, B. & Willcox, K. Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation.
University of Wisconsin-Madison, Technical Report, 2017.
[2] Peherstorfer, B., Willcox, K. & Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization.
Aerospace Computational Design Laboratory, Massachusetts Institute of Technology, Technical Report 16-1, 2016.
Full list

Selected publications

[1] Peherstorfer, B., Gugercin, S. & Willcox, K. Data-driven reduced model construction with time-domain Loewner models.
SIAM Journal on Scientific Computing, 2017. (accepted).
[2] Peherstorfer, B., Kramer, B. & Willcox, K. Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models.
Journal of Computational Physics, 341:61-75, 2017.
[3] Kramer, B., Peherstorfer, B. & Willcox, K. Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models.
SIAM Journal on Applied Dynamical Systems, 2017. (accepted).
[4] Peherstorfer, B., Willcox, K. & Gunzburger, M. Optimal model management for multifidelity Monte Carlo estimation.
SIAM Journal on Scientific Computing, 38(5):A3163-A3194, 2016.
[5] Peherstorfer, B. & Willcox, K. Data-driven operator inference for nonintrusive projection-based model reduction.
Computer Methods in Applied Mechanics and Engineering, 306:196-215, Elsevier, 2016.
[6] Peherstorfer, B., Cui, T., Marzouk, Y. & Willcox, K. Multifidelity Importance Sampling.
Computer Methods in Applied Mechanics and Engineering, 300:490-509, Elsevier, 2016.
[7] Peherstorfer, B. & Willcox, K. Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates.
SIAM Journal on Scientific Computing, 37(4):A2123-A2150, SIAM, 2015.
[8] Peherstorfer, B., Zimmer, S., Zenger, C. & Bungartz, H.J. A Multigrid Method for Adaptive Sparse Grids.
SIAM Journal on Scientific Computing, 37(5):S51-S70, SIAM, 2015.
[9] Peherstorfer, B., Butnaru, D., Willcox, K. & Bungartz, H.J. Localized Discrete Empirical Interpolation Method.
SIAM Journal on Scientific Computing, 36(1):A168-A192, SIAM, 2014.
[10] Pflüger, D., Peherstorfer, B. & Bungartz, H.J. Spatially adaptive sparse grids for high-dimensional data-driven problems.
Journal of Complexity, 26(5):508-522, Academic Press, Inc., 2010.
Full list

Five selected talks

[1] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016.
[2] Peherstorfer, B. Online Adaptive Model Reduction.
In SIAM Conference on Computational Science and Engineering 2015 SIAM, Salt Lake City, USA, 2015.
[3] Peherstorfer, B. Density Estimation with Adaptive Sparse Grids.
In SIAM Uncertainty Quantification 2014 SIAM, Savannah, USA, 2014.
[4] Peherstorfer, B. Localized Discrete Empirical Interpolation Method.
In Second International Workshop on Model Reduction for Parametrized Systems (MoRePaS II) Institut für Informatik, Technische Universität München, Schloss Reisensburg, Günzburg, Germany, 2012.
[5] Peherstorfer, B. A multigrid method for PDEs on spatially adaptive sparse grids.
In 28th GAMM-Seminar on Analysis and Numerical Methods in Higher Dimensions Institut für Informatik, Technische Universität München, Leipzig, Germany, 2012.
Full list

Selected software