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]

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]

Conference publications (peer-reviewed)

[1] Baptista, R., Marzouk, Y., Willcox, K. & Peherstorfer, B. Optimal Approximations of Coupling in Multidisciplinary Models.
In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA, 2017.
[BibTeX]
[2] Peherstorfer, B. & Willcox, K. Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems .
In International Conference on Computational Science, Volume 51 of Procedia Computer Science, pages 2553-2562, Elsevier, 2015.
[BibTeX]
[3] Geuss, M., Butnaru, D., Peherstorfer, B., Bungartz, H.J. & Lohmann, B. Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces.
In European Control Conference (ECC) 2014, IEEE, 2014.
[BibTeX]
[4] Peherstorfer, B., Pflüger, D. & Bungartz, H.J. Density Estimation with Adaptive Sparse Grids for Large Data Sets.
In SIAM Data Mining 2014, SIAM, 2014.
[BibTeX]
[5] Peherstorfer, B., Franzelin, F., Pflüger, D. & Bungartz, H.J. Classification with Probability Density Estimation on Sparse Grids.
In Sparse Grids and Applications 2012, Volume 97 of Lecture Notes in Computational Science and Engineering, 2014. (accepted).
[BibTeX]
[6] Peherstorfer, B., Adorf, J., Pflüger, D. & Bungartz, H.J. Image Segmentation with Adaptive Sparse Grids.
In AI 2013: Advances in Artificial Intelligence, Volume 8272 of Lecture Notes in Computer Science Volume, pages 160-165, Springer, 2013.
[BibTeX]
[7] Bohn, B., Garcke, J., Iza-Teran, R., Paprotny, A., Peherstorfer, B., Schepsmeier, U. & Thole, C.A. Analysis of car crash simulation data with nonlinear machine learning methods.
In International Conference on Computational Science, Volume 18 of Procedia Computer Science, pages 621-630, Elsevier, 2013.
[BibTeX]
[8] Peherstorfer, B., Zimmer, S. & Bungartz, H.J. Model Reduction with the Reduced Basis Method and Sparse Grids.
In Sparse Grids and Applications 2011, Volume 88 of Lecture Notes in Computational Science and Engineering, pages 223-242, Springer, 2013.
[BibTeX]
[9] Butnaru, D., Peherstorfer, B., Pflüger, D. & Bungartz, H.J. Fast Insight into High-Dimensional Parametrized Simulation Data.
In 11th International Conference on Machine Learning and Applications (ICMLA), pages 265-270, IEEE, 2012.
[BibTeX]
[10] Peherstorfer, B., Pflüger, D. & Bungartz, H.J. Clustering Based on Density Estimation with Sparse Grids.
In KI 2012: Advances in Artificial Intelligence, Volume 7526 of Lecture Notes in Computer Science, pages 131-142, Springer, 2012.
[BibTeX]
[11] Heinecke, A., Peherstorfer, B., Pflüger, D. & Song, Z. Sparse Grid Classifiers as Base Learners for AdaBoost.
In International Conference on High Performance Computing and Simulation (HPCS), pages 161-166, IEEE, 2012.
[BibTeX]
[12] Peherstorfer, B. & Bungartz, H.J. Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method.
In International Conference on Computational Science, Volume 9 of Procedia Computer Science, pages 2000-2003, Elsevier, 2012.
[BibTeX]
[13] Peherstorfer, B., Pflüger, D. & Bungartz, H.J. A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.
In AI 2011: Advances in Artificial Intelligence, Volume 7106 of Lecture Notes in Computer Science, pages 112-121, Springer, 2011.
[BibTeX]

PhD thesis

[1] Peherstorfer, B. Model Order Reduction of Parametrized Systems with Sparse Grid Learning Techniques.
Technische Universität München, Munich, Germany, 2013.
[BibTeX]

Talks

[1] Peherstorfer, B. Multifidelity methods for rare event simulation.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[2] Peherstorfer, B. Online adaptive discrete empirical interpolation for nonlinear model reduction.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[3] Peherstorfer, B. Multifidelity methods for uncertainty propagation and rare event simulation.
In QUIET 2017 - Quantification of Uncertainty: Improving Efficiency and Technology SISSA, Trieste, Italy, 2017.
[4] 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.
[5] Peherstorfer, B. Multifidelity Monte Carlo Methods for Rare Event Simulation.
In MATRIX Workshop on Inverse Problems, Melbourne, Australia, 2017.
[6] 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.
[7] Peherstorfer, B. Multifidelity Methods for Uncertainty Propagation and Rare Event Simulation.
In Workshop on Data-Driven Modeling and Uncertainty Quantification (UQPM), Austin, USA, 2017.
[8] Peherstorfer, B. Multifidelity Monte Carlo Methods with Optimally-Adapted Surrogate Models.
In SIAM Computational Science and Engineering 2017, Atlanta, USA, 2017.
[9] Peherstorfer, B. Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation.
In SILO Seminar Wisconsin Institute for Discovery, Madison, USA, 2017.
[10] Peherstorfer, B. Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation.
In Applied and Computational Mathematics Seminar Department of Mathematics, University of Wisconsin-Madison, Madison, USA, 2016.
[11] Peherstorfer, B. Safe and Efficient Data-Driven Model Reduction for Critical Engineering Applications.
In Next Generation Mobility Modeling and Simulation, Novi, USA, 2016.
[12] Peherstorfer, B. Data-Driven Methods for Nonintrusive Model Reduction.
In SIAM Annual Meeting 2016, Boston, USA, 2016.
[13] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In Workshop on Data to Decisions in Aerospace Engineering, Auckland, New Zealand, 2016.
[14] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016.
[15] Peherstorfer, B. Multifidelity Monte Carlo estimation with multiple surrogate models.
In Copper Mountain conference on iterative methods, Copper Mountain, USA, 2016.
[16] Peherstorfer, B. Multifidelity methods for uncertainty quantification.
In Third International Workshop on Model Reduction for Parametrized Systems (MoRePaS III) SISSA, Trieste, Italy, 2015.
[17] Peherstorfer, B. Online adaptive model reduction with dynamic models and sparse sampling.
In European Numerical Mathematics and Advanced Applications (ENUMATH) Middle East Technical University, Ankara, Turkey, 2015.
[18] Peherstorfer, B. Multifidelity Monte Carlo.
In 6th Workshop on High-Dimensional Approximation University of Bonn, Bonn, Germany, 2015.
[19] Peherstorfer, B. Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems.
In International Conference on Computational Science Reykjavík University, Reykjavík, Iceland, 2015.
[20] Peherstorfer, B. Online Adaptive Model Reduction.
In SIAM Conference on Computational Science and Engineering 2015 SIAM, Salt Lake City, USA, 2015.
[21] Peherstorfer, B. Nonlinear model reduction through online adaptivity and dynamic models.
In Scientific Computing Colloquium TUM, Munich, Germany, 2014.
[22] Peherstorfer, B. Online Adaptive Model Reduction for Nonlinear Systems.
In SIAM MIT Chapter 2014 MIT, Boston, USA, 2014.
[23] Peherstorfer, B. Sparse grid density estimation with data independent quantities.
In Sparse Grids and Applications 2014 SimTech, Stuttgart, Germany, 2014.
[24] Peherstorfer, B. Density Estimation with Adaptive Sparse Grids for Large Datasets.
In SIAM Data Mining 2014 SIAM, Philadelphia, USA, 2014.
[25] Peherstorfer, B. Density Estimation with Adaptive Sparse Grids.
In SIAM Uncertainty Quantification 2014 SIAM, Savannah, USA, 2014.
[26] Peherstorfer, B. Localized model order reduction with machine learning methods.
In SIAM and MIT CCE series Center for Computational Engineering, MIT, MIT, Boston, USA, 2014.
[27] Peherstorfer, B. Localized Discrete Empirical Interpolation Method.
In ACDL Seminars Department of Aeronautics and Astronautics, MIT, Department of Aeronautics and Astronautics, MIT, Boston, USA, 2014.
[28] Peherstorfer, B. Localized DEIM based on feature extraction.
In Model Reduction and Approximation for Complex Systems 2013 Institut für Informatik, Technische Universität München, Centre International de Rencontres Mathematiques, Marseille, France, 2013.
[29] Bungartz, H.J. & Peherstorfer, B. Tackling higher dimensionalities with sparse grids.
In ACM/FEF 2013, San Diego, USA, 2013.
[30] Peherstorfer, B. Density Estimation for Large Datasets with Sparse Grids.
In SIAM Conference on Computational Science and Engineering Institut für Informatik, Technische Universität München, Boston, USA, 2013.
[31] Peherstorfer, B. Dünne Gitter: Konstruktion und Anwendung optimaler Diskretisierungen.
In NUMET 2013 Lehrstuhl für Strömungsmechanik (LSTM)Institut für Informatik, Technische Universität München, Lehrstuhl für Strömungsmechanik (LSTM), Universität Erlangen-Nürnberg, Germany, 2013.
[32] Peherstorfer, B. Reduced Order Models with LDEIM for Parametrized PDEs with Nonlinear Terms.
In Angewandte Analysis und Numerische Simulation Institut für Informatik, Technische Universität München, Universität Stuttgart, Germany, 2013.
[33] 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.
[34] Peherstorfer, B. Clustering Based on Density Estimation with Sparse Grids.
In KI 2012: Advances in Artificial Intelligence Institut für Informatik, Technische Universität München, Saarbrücken, Germany, 2012.
[35] Peherstorfer, B. A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.
In SGA 2012 Institut für Informatik, Technische Universität München, Munich, Germany, 2012.
[36] 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.
[37] Peherstorfer, B. Clustering of Truck-Data with Sparse Grids.
In Project Meeting BMBF SIMDATA-NL Institut für Informatik, Technische Universität München, Fraunhofer SCAI, Bonn, Germany, 2011.
[38] Peherstorfer, B. A multigrid method for PDEs on spatially adaptive sparse grids.
In 4th Workshop on High-Dimensional Approximation Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011.
[39] Peherstorfer, B. Reduced Basis Methods and Sparse Grids.
In HIM - Workshop on Sparse Grids and Applications Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011.
[40] Peherstorfer, B. Hierarchical Transformation Multigrid.
In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010.
[41] Peherstorfer, B. Introduction to Reduced Basis Methods.
In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010.