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

Preprints

[1] Uy, W.I.T., Wang, Y., Wen, Y. & Peherstorfer, B. Active operator inference for learning low-dimensional dynamical-system models from noisy data.
arXiv:2107.09256, 2021.
[Abstract] [BibTeX]
[2] Sawant, N., Kramer, B. & Peherstorfer, B. Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference.
arXiv:2107.02597, 2021.
[Abstract] [BibTeX]
[3] Alsup, T., Venturi, L. & Peherstorfer, B. Multilevel Stein variational gradient descent with applications to Bayesian inverse problems.
arXiv:2104.01945, 2021.
[Abstract] [BibTeX]
[4] Konrad, J., Farcas, I.G., Peherstorfer, B., Siena, A.D., Jenko, F., Neckel, T. & Bungartz, H.J. Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis.
arXiv:2103.07539, 2021.
[Abstract] [BibTeX]
[5] Alsup, T. & Peherstorfer, B. Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems.
arXiv:2010.11708, 2020.
[Abstract] [BibTeX]
[6] Rim, D., Venturi, L., Bruna, J. & Peherstorfer, B. Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems.
arXiv:2007.13977, 2020.
[Abstract] [BibTeX]
[7] Rim, D., Peherstorfer, B. & Mandli, K.T. Manifold approximations via transported subspaces: Model reduction for transport-dominated problems.
arXiv:1912.13024, 2019.
[Abstract] [BibTeX]

Journal publications

[1] Uy, W.I.T. & Peherstorfer, B. Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories.
Journal of Scientific Computing, 2021. (accepted).
[Abstract] [BibTeX]
[2] Uy, W.I.T. & Peherstorfer, B. Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations.
ESAIM: Mathematical Modelling and Numerical Analysis (M2AN), 2021. (accepted).
[Abstract] [BibTeX]
[3] Peherstorfer, B. Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference.
SIAM Journal on Scientific Computing, 42:A3489-A3515, 2020.
[Abstract] [BibTeX]
[4] Benner, P., Goyal, P., Kramer, B., Peherstorfer, B. & Willcox, K. Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms.
Computer Methods in Applied Mechanics and Engineering, 372, 2020.
[Abstract] [BibTeX]
[5] Peherstorfer, B., Drmac, Z. & Gugercin, S. Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points.
SIAM Journal on Scientific Computing, 42:A2837-A2864, 2020.
[Abstract] [BibTeX]
[6] Peherstorfer, B. Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling.
SIAM Journal on Scientific Computing, 42:A2803-A2836, 2020.
[Abstract] [BibTeX]
[7] Qian, E., Kramer, B., Peherstorfer, B. & Willcox, K. Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems.
Physica D: Nonlinear Phenomena, Volume 406, 2020.
[Abstract] [BibTeX]
[8] Peherstorfer, B. & Marzouk, Y. A transport-based multifidelity preconditioner for Markov chain Monte Carlo.
Advances in Computational Mathematics, 45:2321-2348, 2019.
[Abstract] [BibTeX]
[9] Peherstorfer, B. Multifidelity Monte Carlo estimation with adaptive low-fidelity models.
SIAM/ASA Journal on Uncertainty Quantification, 7:579-603, 2019.
[Abstract] [BibTeX]
[10] 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]
[11] 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]
[12] 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]
[13] Peherstorfer, B., Gunzburger, M. & Willcox, K. Convergence analysis of multifidelity Monte Carlo estimation.
Numerische Mathematik, 139(3):683-707, 2018.
[Abstract] [BibTeX]
[14] 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]
[15] Baptista, R., Marzouk, Y., Willcox, K. & Peherstorfer, B. Optimal Approximations of Coupling in Multidisciplinary Models.
AIAA Journal, 56:2412-2428, 2018.
[Abstract] [BibTeX]
[16] 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]
[17] 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]
[18] 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]
[19] 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]
[20] 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]
[21] 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]
[22] 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]
[23] 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]
[24] Peherstorfer, B., Cui, T., Marzouk, Y. & Willcox, K. Multifidelity Importance Sampling.
Computer Methods in Applied Mechanics and Engineering, 300:490-509, 2016.
[Abstract] [BibTeX]
[25] 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]
[26] 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]
[27] Peherstorfer, B. & Willcox, K. Dynamic Data-Driven Reduced-Order Models.
Computer Methods in Applied Mechanics and Engineering, 291:21-41, 2015.
[Abstract] [BibTeX]
[28] 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]
[29] 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]
[30] 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]
[31] 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]

Conference publications (peer-reviewed)

[1] Drmac, Z. & Peherstorfer, B. Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation.
In Realization and Model Reduction of Dynamical Systems: A Festschrift in Honor of the 70th Birthday of Thanos Antoulas, Springer, 2020.
[Abstract] [BibTeX]
[2] Cortinovis, A., Kressner, D., Massei, S. & Peherstorfer, B. Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation.
In American Control Conference (ACC) 2020, IEEE, 2020.
[Abstract] [BibTeX]
[3] Chaudhuri, A., Peherstorfer, B. & Willcox, K. Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization.
In AIAA Scitech 2020 Forum, AIAA, 2020.
[Abstract] [BibTeX]
[4] Peherstorfer, B., Beran, P.S. & Willcox, K. Multifidelity Monte Carlo estimation for large-scale uncertainty propagation.
In 2018 AIAA Non-Deterministic Approaches Conference, AIAA, 2018.
[Abstract] [BibTeX]
[5] 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]
[6] 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]
[7] 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]
[8] Peherstorfer, B., Pflüger, D. & Bungartz, H.J. Density Estimation with Adaptive Sparse Grids for Large Data Sets.
In SIAM Data Mining 2014, SIAM, 2014.
[Abstract] [BibTeX]
[9] 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]
[10] 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]
[11] 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]
[12] 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]
[13] 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]
[14] 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]
[15] 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]
[16] 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]
[17] 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. Context-aware model reduction for uncertainty quantification.
In SIAM Annual Meeting 2021, online, 2021.
[2] Peherstorfer, B. Scientific machine learning with operator inference and re-projection.
In ISC High Performance 2021, online, 2021.
[3] Peherstorfer, B. Scientific machine learning with operator inference and re-projection.
In Next Generation Simulation seminar series, Siemens AG, online, 2021.
[4] Peherstorfer, B. Modeling nonlinear low-dimensional dynamics with deep networks.
In Mathematical Modeling and Simulation Seminar, Courant Institute, online, 2021.
[5] Peherstorfer, B. Scientific Machine Learning with Operator Inference and Re-Projection.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[6] Peherstorfer, B. Nonlinear model reduction for transport-dominated problems.
In Applied Mathematics Seminar, Courant Institute, online, 2021.
[7] Peherstorfer, B. Scientific machine learning with operator inference and re-projection.
In Aerospace Computational Design Laboratory Seminar, Massachusetts Institute of Technology, Cambridge, MA, 2020.
[8] Peherstorfer, B. Nonlinear model reduction for transport-dominated problems.
In Numerical Analysis and PDE Seminar, University of Delaware, Newark, DE, 2020.
[9] Peherstorfer, B. A biased introduction to projection-based model reduction.
In Descriptors of Energy Landscapes Using Topological Data Analysis Seminar Series, online, 2020.
[10] Peherstorfer, B. Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation.
In American Control Conference (ACC) 2020, Denver, CO, 2020.
[11] Peherstorfer, B. Learning low-dimensional dynamical-system models from data via non-intrusive model reduction.
In MAC-MIGS afternoon on Randomness and Data, Edinburgh, United Kingdom, 2020.
[12] Peherstorfer, B. Context-aware learning of surrogate models for multi-fidelity computations.
In Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data, Vienna, Austria, 2020.
[13] Peherstorfer, B. Sampling low-dimensional Markovian dynamics for learning certified reduced models from data.
In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020.
[14] Peherstorfer, B. Learning dynamical-system models from data with time-domain Loewner, dynamic mode decomposition, and operator inference.
In Model and dimension reduction in uncertain and dynamic systems, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020.
[15] Peherstorfer, B. Recovering certified reduced dynamical-system models from data with operator inference.
In SIAM Minisymposium on Applications of Machine Learning to the Analysis of Nonlinear Dynamical Systems at Joint Mathematics Meetings 2020, Denver, CO, 2020.
[16] Peherstorfer, B. Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization.
In AIAA SciTech 2020, Orlando, FL, 2020.
[17] Peherstorfer, B. Recovering (certified) reduced models from data with operator inference and time-domain Loewner.
In European Numerical Mathematics and Advanced Applications Conference, Amsterdam, Netherlands, 2019.
[18] Peherstorfer, B. Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data.
In Computational Science Initiative, Brookhaven National Laboratory, Brookhaven, NY, 2019.
[19] Peherstorfer, B. Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data.
In Workshop on Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific Computing, University of Heidelberg, Heidelberg, Germany, 2019.
[20] Peherstorfer, B. Data generation and time-delay corrections for learning reduced models with operator inference.
In Physics Informed Machine Learning Workshop, Seattle, WA, 2019.
[21] Peherstorfer, B. Learning reduced dynamical-system models from data via operator inference and Loewner interpolation.
In Oden Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, Austin, TX, 2019.
[22] Peherstorfer, B. Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates.
In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019.
[23] Peherstorfer, B. Model reduction for transport-dominated problems via adaptive basis updates.
In Applied Mathematics Colloquium, Columbia, New York, NY, 2018.
[24] Peherstorfer, B. Learning Context-Aware Reduced Models for Multifidelity Computations.
In School for Simulation and Data Sciences, RWTH Aachen, Aachen, Germany, 2018.
[25] Peherstorfer, B. Context-Aware Model Reduction.
In Computational Mathematics and Simulation Science Seminar, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2018.
[26] Peherstorfer, B. Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates.
In Numerical Analysis and Scientific Computing Seminar, Courant Institute, New York, NY, 2018.
[27] Peherstorfer, B. Data-Driven Multifidelity Methods for Monte Carlo Estimation.
In Workshop on Big Data Meets Large-Scale Computing, Institute for Pure & Applied Mathematics (IPAM), Los Angeles, CA, 2018.
[28] Peherstorfer, B. Learning context-aware surrogate models for multifidelity uncertainty quantification.
In World Congress in Computational Mechanics, New York, NY, 2018.
[29] Peherstorfer, B. Multifidelity methods and context-aware model reduction for Monte Carlo estimation and beyond.
In Seminar Numerische Mathematik, Technical University Berlin, Berlin, Germany, 2018.
[30] Peherstorfer, B. A Multifidelity Cross-Entropy Method for Rare Event Simulation.
In SIAM Uncertainty Quantification 2018, Garden Grove, CA, 2018.
[31] Peherstorfer, B. Data-Driven Multifidelity Methods for Monte Carlo Estimation.
In Model Reduction of Parametrized Systems (MoRePaS) IV, Nantes, France, 2018.
[32] 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.
[33] Peherstorfer, B. Data-Driven Multifidelity Methods for Monte Carlo Estimation.
In Engineering Physics Seminars and Colloquium, University of Wisconsin-Madison, Madison, USA, 2018.
[34] Peherstorfer, B. Multifidelity Monte Carlo estimation for large-scale uncertainty propagation.
In 2018 AIAA Non-Deterministic Approaches Conference (AIAA SciTech), Kissimmee, USA, 2018.
[35] Peherstorfer, B. Multifidelity methods for rare event simulation.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[36] Peherstorfer, B. Online adaptive discrete empirical interpolation for nonlinear model reduction.
In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017.
[37] 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.
[38] 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.
[39] Peherstorfer, B. Multifidelity Monte Carlo Methods for Rare Event Simulation.
In MATRIX Workshop on Inverse Problems, Melbourne, Australia, 2017.
[40] 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.
[41] Peherstorfer, B. Multifidelity Methods for Uncertainty Propagation and Rare Event Simulation.
In Workshop on Data-Driven Modeling and Uncertainty Quantification (UQPM), Austin, USA, 2017.
[42] Peherstorfer, B. Multifidelity Monte Carlo Methods with Optimally-Adapted Surrogate Models.
In SIAM Computational Science and Engineering 2017, Atlanta, USA, 2017.
[43] Peherstorfer, B. Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation.
In SILO Seminar Wisconsin Institute for Discovery, Madison, USA, 2017.
[44] 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.
[45] Peherstorfer, B. Safe and Efficient Data-Driven Model Reduction for Critical Engineering Applications.
In Next Generation Mobility Modeling and Simulation, Novi, USA, 2016.
[46] Peherstorfer, B. Data-Driven Methods for Nonintrusive Model Reduction.
In SIAM Annual Meeting 2016, Boston, USA, 2016.
[47] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In Workshop on Data to Decisions in Aerospace Engineering, Auckland, New Zealand, 2016.
[48] Peherstorfer, B. Multifidelity Methods for Uncertainty Quantification.
In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016.
[49] Peherstorfer, B. Multifidelity Monte Carlo estimation with multiple surrogate models.
In Copper Mountain conference on iterative methods, Copper Mountain, USA, 2016.
[50] Peherstorfer, B. Multifidelity methods for uncertainty quantification.
In Third International Workshop on Model Reduction for Parametrized Systems (MoRePaS III) SISSA, Trieste, Italy, 2015.
[51] 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.
[52] Peherstorfer, B. Multifidelity Monte Carlo.
In 6th Workshop on High-Dimensional Approximation University of Bonn, Bonn, Germany, 2015.
[53] 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.
[54] Peherstorfer, B. Online Adaptive Model Reduction.
In SIAM Conference on Computational Science and Engineering 2015 SIAM, Salt Lake City, USA, 2015.
[55] Peherstorfer, B. Nonlinear model reduction through online adaptivity and dynamic models.
In Scientific Computing Colloquium TUM, Munich, Germany, 2014.
[56] Peherstorfer, B. Online Adaptive Model Reduction for Nonlinear Systems.
In SIAM MIT Chapter 2014 MIT, Boston, USA, 2014.
[57] Peherstorfer, B. Sparse grid density estimation with data independent quantities.
In Sparse Grids and Applications 2014 SimTech, Stuttgart, Germany, 2014.
[58] Peherstorfer, B. Density Estimation with Adaptive Sparse Grids for Large Datasets.
In SIAM Data Mining 2014 SIAM, Philadelphia, USA, 2014.
[59] Peherstorfer, B. Density Estimation with Adaptive Sparse Grids.
In SIAM Uncertainty Quantification 2014 SIAM, Savannah, USA, 2014.
[60] 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.
[61] 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.
[62] 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.
[63] Bungartz, H.J. & Peherstorfer, B. Tackling higher dimensionalities with sparse grids.
In ACM/FEF 2013, San Diego, USA, 2013.
[64] 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.
[65] 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.
[66] 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.
[67] 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.
[68] 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.
[69] 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.
[70] 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.
[71] 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.
[72] 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.
[73] 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.
[74] Peherstorfer, B. Hierarchical Transformation Multigrid.
In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010.
[75] Peherstorfer, B. Introduction to Reduced Basis Methods.
In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010.

Talks and posters presented by group members

[1] Uy, W. Operator Inference for Learning Reduced Models with Non-Markovian Terms from Partially Observed State Trajectories.
In SIAM Annual Meeting 2021, online, 2021.
[2] Shyamkumar, N. Context-Aware Learning of Models for Data-Driven Robust Control.
In SIAM Conference on Control and Its Applications 2021, online, 2021.
[3] Law, F. Learning Data-Fit Models for Multi-Fidelity Monte Carlo Estimation of Energetic Particle Loss in Fusion Reactors.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[4] Rim, D. Reduced Deep Networks: Distilling Nonlinear Shock Waves.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[5] Alsup, T. Trading-off Deterministic Preconditioning and Sampling in Bayesian Inference.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[6] Alsup, T. Learning Context-Aware Surrogate Models for Multifidelity Importance Sampling and Bayesian Inverse Problems.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[7] Uy, W. Certified Predictions from Data: A Posteriori Error Estimators for Non-Intrusive Reduced Models Learned from Data.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021.
[8] Sawant, N. Learning Locally Stable Low-Dimensional Dynamical-System Models with Quadratic Nonlinear Terms from State Trajectories with Operator Inference.
In SIAM Conference on Computational Science and Engineering 2021, online, 2021. (poster).
[9] Uy, W. Operator inference: A posteriori error estimation for reduced models learned from data and non-Markovian correction terms.
In Numerical Analysis and Scientific Computing seminar, Courant Institute, New York University, online, 2020.
[10] Rim, D. Low-rank transport for 2D waves: A dimensional splitting approach.
In SIAM Conference on Mathematics of Data Science (MDS20), online, 2020.
[11] Rim, D. Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems.
In Mathematics Colloquium, University of Central Florida, Orlando, FL, 2020.
[12] Rim, D. Manifold Approximation via Transported Subspaces (MATS).
In Computational Mathematics Seminar, University of Pittsburgh, Pittsburgh, PA, 2019.
[13] Alsup, T. & Peherstorfer, B. Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems.
In Computational Statistics and Data-Driven Modeling, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020.
[14] Alsup, T. & Peherstorfer, B. Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems.
In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020.
[15] Rim, D., Peherstorfer, B. & Mandli, K.T. Manifold Approximations via Transported Subspaces.
In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020.
[16] Uy, W.I.T. & Peherstorfer, B. Pre-asymptotic error estimation for reduced dynamical-system models learned from data.
In Optimization and Inversion under Uncertainty, Johann Radon Institute, Linz, Austria, 2019.
[17] Rim, D., Peherstorfer, B. & Mandli, K.T. Model Reduction of Nonlinear Hyperbolic Problems Using Low-dimensional Transport Modes.
In European Numerical Mathematics and Advanced Applications Conference, Amsterdam, Netherlands, 2019.
[18] Sawant, N. & Peherstorfer, B. Data generation and stabilization for reduced modeling with operator inference.
In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019.