University of Wisconsin-Madison ME | Math | WID
Benjamin Peherstorfer
Assistant Professor

Department of Mechanical Engineering
Department of Mathematics (by courtesy)
Wisconsin Institute for Discovery (affiliated)
University of Wisconsin-Madison

A0407-1513 University Ave
Madison, WI 53706

E-mail: peherstorfer at wisc.edu
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

News

Apr 2018:   Invited speaker at the Model Reduction for Parametrized Systems (MoRePaS) IV conference
Sep 2017:   Our survey paper on multifidelity methods for outer-loop applications has been accepted by SIAM Review
Jul 2017:   Our work on data-driven nonintrusive model reduction with operator inference is cited in SIAM News
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.
[BibTeX]
[2] Qian, E., Peherstorfer, B., O'Malley, D., Vesselinov, V.V. & Willcox, K. Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices.
Massachusetts Institute of Technology, ACDL TR-2017-2, 2017.
[BibTeX]
[3] Kramer, B., Marques, A., Peherstorfer, B., Villa, U. & Willcox, K. Multifidelity probability estimation via fusion of estimators.
Massachusetts Institute of Technology, ACDL TR-2017-3, 2017.
[BibTeX]
[4] Peherstorfer, B., Gunzburger, M. & Willcox, K. Convergence analysis of multifidelity Monte Carlo estimation.
University of Wisconsin-Madison, Technical Report, 2016.
[BibTeX]
Full list

Journal publications

[1] Zimmermann, R., Peherstorfer, B. & Willcox, K. Geometric subspace updates with applications to online adaptive nonlinear model reduction.
SIAM Journal on Matrix Analysis and Applications, 2017. (accepted).
[BibTeX]
[2] Peherstorfer, B., Willcox, K. & Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization.
SIAM Review, 2017. (accepted).
[BibTeX]
[3] Peherstorfer, B., Gugercin, S. & Willcox, K. Data-driven reduced model construction with time-domain Loewner models.
SIAM Journal on Scientific Computing, 2017. (accepted).
[BibTeX]
[4] 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.
[BibTeX]
[5] Kramer, B., Peherstorfer, B. & Willcox, K. Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models.
SIAM Journal on Applied Dynamical Systems, 16(3):1563-1586, 2017.
[BibTeX]
[6] Peherstorfer, B., Willcox, K. & Gunzburger, M. Optimal model management for multifidelity Monte Carlo estimation.
SIAM Journal on Scientific Computing, 38(5):A3163-A3194, 2016.
[BibTeX]
[7] 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.
[BibTeX]
[8] Peherstorfer, B. & Willcox, K. Dynamic data-driven model reduction: Adapting reduced models from incomplete data.
Advanced Modeling and Simulation in Engineering Sciences, 3(11), Springer, 2016.
[BibTeX]
[9] Peherstorfer, B., Cui, T., Marzouk, Y. & Willcox, K. Multifidelity Importance Sampling.
Computer Methods in Applied Mechanics and Engineering, 300:490-509, Elsevier, 2016.
[BibTeX]
[10] 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.
[BibTeX]
[11] Peherstorfer, B., Gómez, P. & Bungartz, H.J. Reduced Models for Sparse Grid Discretizations of the Multi-Asset Black-Scholes Equation.
Advances in Computational Mathematics, 41(5):1365-1389, Springer, 2015.
[BibTeX]
[12] Peherstorfer, B. & Willcox, K. Dynamic Data-Driven Reduced-Order Models.
Computer Methods in Applied Mechanics and Engineering, 291:21-41, Elsevier, 2015.
[BibTeX]
[13] 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.
[BibTeX]
[14] 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.
[BibTeX]
[15] Peherstorfer, B., Kowitz, C., Pflüger, D. & Bungartz, H.J. Selected Recent Applications of Sparse Grids.
Numerical Mathematics: Theory, Methods and Applications, 8(1):47-77, GSP, 2014.
[BibTeX]
[16] 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.
[BibTeX]
Full list

Five selected talks

[1] Peherstorfer, B. Multifidelity methods for rare event simulation.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[2] Peherstorfer, B. Optimal low-rank updates for online adaptive model reduction with the discrete empirical interpolation method.
In Householder Symposium XX on Numerical Linear Algebra, Blacksburg, USA, 2017.
[3] Peherstorfer, B. Data-driven reduced model construction with the time-domain Loewner framework and operator inference.
In Colloquium, Department of Mathematics, Virginia Tech, Blacksburg, USA, 2017.
[4] Peherstorfer, B. Multifidelity Monte Carlo Methods with Optimally-Adapted Surrogate Models.
In SIAM Computational Science and Engineering 2017, Atlanta, USA, 2017.
[5] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016.
Full list

Selected software