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
  • I will join Courant Institute of Mathematical Sciences at New York University as assistant professor on September 1, 2018.

  • There will be a PostDoc position open at Courant Institute in my group. The research focus is on data-driven model reduction and/or multifidelity methods and/or Bayesian methods for uncertainty quantification. Interested candidates should apply to Benjamin Peherstorfer under peherstorfer at wisc dot edu (and as of Sep 1, 2018 under pehersto at cims dot nyu dot edu) and include in PDF format (1) a CV with names of up to three references, (2) a statement of research experience, interests, and goals, and (3) links to up to three indicative publications/preprints.

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

Sep 2018:   I will join Courant Institute of Mathematical Sciences at New York University as assistant professor on September 1, 2018
Sep 2018:   Invited to speak in the program "Science at Extreme Scales: Where Big Data Meets Large-Scale Computing" at the Institute for Pure & Applied Mathematics (IPAM)
Apr 2018:   Co-organizer of minisymposium on multilevel and multifidelity methods for Bayesian inverse problems at SIAM Uncertainty Quantification 2018; with Tiangang Cui (Monash University)
Apr 2018:   Invited speaker at the Model Reduction of Parametrized Systems (MoRePaS) IV conference
Mar 2018:   Invited to speak at the workshop "Reducing dimensions and cost for UQ in complex systems", which is held at the Isaac Newton Institute for Mathematical Sciences
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 journal publications

[1] Peherstorfer, B., Kramer, B. & Willcox, K. Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation.
SIAM/ASA Journal on Uncertainty Quantification, 6(2):737-761, 2018.
[Abstract] [BibTeX]
[2] Peherstorfer, B., Gunzburger, M. & Willcox, K. Convergence analysis of multifidelity Monte Carlo estimation.
Numerische Mathematik, 139(3):683-707, 2018.
[Abstract] [BibTeX]
[3] Qian, E., Peherstorfer, B., O'Malley, D., Vesselinov, V.V. & Willcox, K. Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices.
SIAM/ASA Journal on Uncertainty Quantification, 6(2):683-706, 2018.
[Abstract] [BibTeX]
[4] Baptista, R., Marzouk, Y., Willcox, K. & Peherstorfer, B. Optimal Approximations of Coupling in Multidisciplinary Models.
AIAA Journal, 56:2412-2428, 2018.
[Abstract] [BibTeX]
[5] Zimmermann, R., Peherstorfer, B. & Willcox, K. Geometric subspace updates with applications to online adaptive nonlinear model reduction.
SIAM Journal on Matrix Analysis and Applications, 39(1):234-261, 2018.
[Abstract] [BibTeX]
[6] Peherstorfer, B., Willcox, K. & Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization.
SIAM Review, 2017. (accepted).
[Abstract] [BibTeX]
[7] Peherstorfer, B., Gugercin, S. & Willcox, K. Data-driven reduced model construction with time-domain Loewner models.
SIAM Journal on Scientific Computing, 39(5):A2152-A2178, 2017.
[Abstract] [BibTeX]
[8] 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.
[Abstract] [BibTeX]
[9] 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.
[Abstract] [BibTeX]
[10] Peherstorfer, B., Willcox, K. & Gunzburger, M. Optimal model management for multifidelity Monte Carlo estimation.
SIAM Journal on Scientific Computing, 38(5):A3163-A3194, 2016.
[Abstract] [BibTeX]
[11] Peherstorfer, B. & Willcox, K. Data-driven operator inference for nonintrusive projection-based model reduction.
Computer Methods in Applied Mechanics and Engineering, 306:196-215, 2016.
[Abstract] [BibTeX]
[12] Peherstorfer, B. & Willcox, K. Dynamic data-driven model reduction: Adapting reduced models from incomplete data.
Advanced Modeling and Simulation in Engineering Sciences, 3(11), 2016.
[Abstract] [BibTeX]
[13] Peherstorfer, B., Cui, T., Marzouk, Y. & Willcox, K. Multifidelity Importance Sampling.
Computer Methods in Applied Mechanics and Engineering, 300:490-509, 2016.
[Abstract] [BibTeX]
[14] Peherstorfer, B. & Willcox, K. Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates.
SIAM Journal on Scientific Computing, 37(4):A2123-A2150, 2015.
[Abstract] [BibTeX]
[15] 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, 2015.
[Abstract] [BibTeX]
[16] Peherstorfer, B. & Willcox, K. Dynamic Data-Driven Reduced-Order Models.
Computer Methods in Applied Mechanics and Engineering, 291:21-41, 2015.
[Abstract] [BibTeX]
[17] 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, 2015.
[Abstract] [BibTeX]
[18] Peherstorfer, B., Butnaru, D., Willcox, K. & Bungartz, H.J. Localized Discrete Empirical Interpolation Method.
SIAM Journal on Scientific Computing, 36(1):A168-A192, 2014.
[Abstract] [BibTeX]
[19] 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, 2014.
[Abstract] [BibTeX]
[20] Pflüger, D., Peherstorfer, B. & Bungartz, H.J. Spatially adaptive sparse grids for high-dimensional data-driven problems.
Journal of Complexity, 26(5):508-522, 2010.
[Abstract] [BibTeX]
Full list

Five recent talks

[1] Peherstorfer, B. A Multifidelity Cross-Entropy Method for Rare Event Simulation.
In SIAM Uncertainty Quantification 2018, Garden Grove, CA, 2018.
[2] Peherstorfer, B. Data-Driven Multifidelity Methods for Monte Carlo Estimation.
In Model Reduction of Parametrized Systems (MoRePaS) IV, Nantes, France, 2018.
[3] Peherstorfer, B. Multifidelity Monte Carlo estimation with adaptive low-fidelity models.
In Reducing dimensions and cost for UQ in complex systems, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, 2018.
[4] Peherstorfer, B. Multifidelity methods for rare event simulation.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[5] 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.
Full list

Selected software