Benjamin Peherstorfer   |   Courant Institute of Mathematical Sciences, New York University
Benjamin Peherstorfer

Assistant Professor
Department of Computer Science | Department of Mathematics (affiliated) | Center for Data Science (affiliated)

Courant Institute of Mathematical Sciences | New York University
251 Mercer Street, New York, NY 10012

Research (Google Scholar)
computational mathematics, machine learning, computational statistics, numerical analysis, and scientific computing

Contact pehersto at cims dot nyu dot edu | 212 998 3297 | Warren Weaver Hall (WWH), Room 421

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News

Jul 2020
Invited to present our work on multifidelity methods as a keynote lecture in the minisymposium Multifidelity Optimization at the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS).
Jul 2020
Jun 2020
Invited to give a tutorial presentation on multifidelity methods for uncertainty quantification at the workshop Multifidelity Modeling in Support of Design and Uncertainty Quantification that is organized as part of the AIAA Aviation Forum.
May 2020
Co-organizer, with Themistoklis Sapsis (MIT), of minisymposium "Learning Dynamical-System Models for Prediction and Control" at the SIAM Conference on Mathematics of Data Science 2020.
Apr 2020
Co-organizer of ICERM workshop on Computational Statistics and Data-Driven Models, which will be held virtually April 20-24, 2020.
Mar 2020
Co-organizer with Youssef Marzouk (MIT) and Yaoliang Yu (U Waterloo) of minisymposium on "Inference and preconditioning via Stein methods, flows, and other transport maps" at the SIAM Conference on Uncertainty Quantification 2020.
Feb 2020
Invited to give a presentation on model reduction for transport-dominated problems at workshop on "Mathematics of Reduced Order Models", which is organized at the Institute for Computational and Experimental Research in Mathematics (ICERM).
Jan 2020
Invited to give a tutorial presentation on learning dynamical-system models from data as part of the program "Model and dimension reduction in uncertain and dynamic systems" at the Institute for Computational and Experimental Research in Mathematics (ICERM).
Aug 2019
Invited to give a presentation in the seminar of the Computational Science Initiative at Brookhaven National Laboratory.
Jun 2019
Invited to give a presentation at the Physics Informed Machine Learning Workshop at University of Washington.
Jun 2019
Invited to give a tutorial presentation for aerospace engineers on our multifidelity uncertainty quantification work at the AIAA Aviation workshop on Multifidelity Modeling In Support Of Design And Uncertainty Quantification.
Mar 2019
There currently are 2 articles that I co-authored under the top-20 downloads of the SIAM/ASA Journal on Uncertainty Quantification (March 30, 2019). The articles are on multifidelity sensitivity analysis (authors Qian, P., O'Malley, Vesselinov, and Willcox) and on multifidelity rare event simulation (authors P., Kramer, and Willcox).
Mar 2019
Invited to give a presentation in the seminar series of the Institute for Computational Engineering and Sciences (ICES) at University of Texas, Austin.
Feb 2019
SIAM selected our minisymposium "Model Reduction for Problems with Strong Convection, Sharp Gradients, and Discontinuities" (co-organized with Maciej Balajewicz and Gerrit Welper) to be a Featured Minisymposium at the SIAM Conference on Computational Science and Engineering 2019.
Nov 2018
Invited to give a presentation in the Applied Mathematics Colloquium at Columbia University.
Oct 2018
Invited to give a presentation in the seminar of the Mathematics in Computational Science and Engineering group at EPFL.
Sep 2018
I will join Courant Institute of Mathematical Sciences at New York University as assistant professor on September 1, 2018.
Sep 2018
I have been selected for a Department of Energy (DoE) Early Career Award in the Applied Mathematics Program (Office of Advanced Scientific Computing Research). My move to Courant Institute (New York University) delayed the process to Fall 2018 and so my grant is not part of the official announcement posted in Summer 2018.
Jul 2018
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
Sep 2017
Our survey paper on multifidelity methods for outer-loop applications has been accepted by SIAM Review.
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).
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.
Nov 2015
Invited talk in the seminar series of the Transregional Collaborative Research Center on Invasive Computing.
Dec 2014
I was awarded the Heinz Schwärtzel prize for my PhD thesis.
Dec 2014
Jan 2014
Started as Postdoctoral Associate in the group of Karen Willcox at MIT..

Selected preprints and submitted articles

[1] Peherstorfer, B. Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference.
arXiv:1908.11233, 2019.
[Abstract] [BibTeX]
[2] Benner, P., Goyal, P., Kramer, B., Peherstorfer, B. & Willcox, K. Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms.
arXiv:2002.09726, 2020.
[Abstract] [BibTeX]
[3] Rim, D., Peherstorfer, B. & Mandli, K.T. Manifold approximations via transported subspaces: Model reduction for transport-dominated problems.
arXiv:1912.13024, 2019.
[Abstract] [BibTeX]
[4] Drmac, Z. & Peherstorfer, B. Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation.
arXiv:1910.00110, 2019.
[Abstract] [BibTeX]
[5] Peherstorfer, B. Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling.
arXiv:1812.02094, 2018.
[Abstract] [BibTeX]
[6] Peherstorfer, B., Drmac, Z. & Gugercin, S. Stabilizing discrete empirical interpolation via randomized and deterministic oversampling.
arXiv:1808.10473, 2018.
[Abstract] [BibTeX]
Full list

Selected journal publications

[1] Qian, E., Kramer, B., Peherstorfer, B. & Willcox, K. Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems.
Physica D: Nonlinear Phenomena, 2020. (accepted).
[Abstract] [BibTeX]
[2] Peherstorfer, B. & Marzouk, Y. A transport-based multifidelity preconditioner for Markov chain Monte Carlo.
Advances in Computational Mathematics, 45:2321-2348, 2019.
[Abstract] [BibTeX]
[3] Peherstorfer, B. Multifidelity Monte Carlo estimation with adaptive low-fidelity models.
SIAM/ASA Journal on Uncertainty Quantification, 7:579-603, 2019.
[Abstract] [BibTeX]
[4] Kramer, B., Marques, A., Peherstorfer, B., Villa, U. & Willcox, K. Multifidelity probability estimation via fusion of estimators.
Journal of Computational Physics, 392:385-402, 2019.
[Abstract] [BibTeX]
[5] Swischuk, R., Mainini, L., Peherstorfer, B. & Willcox, K. Projection-based model reduction: Formulations for physics-based machine learning.
Computers & Fluids, 179:704-717, 2019.
[Abstract] [BibTeX]
[6] 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]
[7] Peherstorfer, B., Gunzburger, M. & Willcox, K. Convergence analysis of multifidelity Monte Carlo estimation.
Numerische Mathematik, 139(3):683-707, 2018.
[Abstract] [BibTeX]
[8] 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]
[9] Baptista, R., Marzouk, Y., Willcox, K. & Peherstorfer, B. Optimal Approximations of Coupling in Multidisciplinary Models.
AIAA Journal, 56:2412-2428, 2018.
[Abstract] [BibTeX]
[10] 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]
[11] Peherstorfer, B., Willcox, K. & Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization.
SIAM Review, 60(3):550-591, 2018.
[Abstract] [BibTeX]
[12] 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]
[13] 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]
[14] 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]
[15] 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]
[16] 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]
[17] 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]
[18] Peherstorfer, B., Cui, T., Marzouk, Y. & Willcox, K. Multifidelity Importance Sampling.
Computer Methods in Applied Mechanics and Engineering, 300:490-509, 2016.
[Abstract] [BibTeX]
[19] 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]
[20] 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]
[21] Peherstorfer, B. & Willcox, K. Dynamic Data-Driven Reduced-Order Models.
Computer Methods in Applied Mechanics and Engineering, 291:21-41, 2015.
[Abstract] [BibTeX]
[22] 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]
[23] 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]
[24] 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]
[25] 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 selected talks

[1] Peherstorfer, B. Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates.
In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019.
[2] Peherstorfer, B. A Multifidelity Cross-Entropy Method for Rare Event Simulation.
In SIAM Uncertainty Quantification 2018, Garden Grove, CA, 2018.
[3] Peherstorfer, B. Data-Driven Multifidelity Methods for Monte Carlo Estimation.
In Model Reduction of Parametrized Systems (MoRePaS) IV, Nantes, France, 2018.
[4] 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.
[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