| Survey of multifidelity methods in uncertainty propagation, inference, and optimization B Peherstorfer, K Willcox, M Gunzburger SIAM Review 60 (3), 550-591, 2018 | 796 | 2018 |
| Data-driven operator inference for nonintrusive projection-based model reduction B Peherstorfer, K Willcox Computer Methods in Applied Mechanics and Engineering 306, 196-215, 2016 | 292 | 2016 |
| Projection-based model reduction: Formulations for physics-based machine learning R Swischuk, L Mainini, B Peherstorfer, K Willcox Computers & Fluids 179, 704-717, 2019 | 271 | 2019 |
| Localized discrete empirical interpolation method B Peherstorfer, D Butnaru, K Willcox, HJ Bungartz SIAM Journal on Scientific Computing 36 (1), 2014 | 242 | 2014 |
| Optimal model management for multifidelity Monte Carlo estimation B Peherstorfer, K Willcox, M Gunzburger SIAM Journal on Scientific Computing 38 (5), A3163-A3194, 2016 | 228 | 2016 |
| Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems E Qian, B Kramer, B Peherstorfer, K Willcox Physica D: Nonlinear Phenomena 406, 132401, 2020 | 185 | 2020 |
| Dynamic data-driven reduced-order models B Peherstorfer, K Willcox Computer Methods in Applied Mechanics and Engineering 291, 21-41, 2015 | 178 | 2015 |
| Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates B Peherstorfer, K Willcox SIAM Journal on Scientific Computing 37 (4), A2123-A2150, 2015 | 163 | 2015 |
| Multifidelity importance sampling B Peherstorfer, T Cui, Y Marzouk, K Willcox Computer Methods in Applied Mechanics and Engineering, 2015 | 117 | 2015 |
| Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling B Peherstorfer SIAM Journal on Scientific Computing 42 (5), A2803-A2836, 2020 | 98 | 2020 |
| Spatially adaptive sparse grids for high-dimensional data-driven problems D Pflüger, B Peherstorfer, HJ Bungartz Journal of Complexity 26 (5), 508-522, 2010 | 93 | 2010 |
| Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms P Benner, P Goyal, B Kramer, B Peherstorfer, K Willcox Computer Methods in Applied Mechanics and Engineering 372, 113433, 2020 | 77 | 2020 |
| Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods B Bohn, J Garcke, R Iza-Teran, A Paprotny, B Peherstorfer, ... Procedia Computer Science 18, 621-630, 2013 | 76 | 2013 |
| Data-Driven Reduced Model Construction with Time-Domain Loewner Models B Peherstorfer, S Gugercin, K Willcox SIAM Journal on Scientific Computing 39 (5), A2152-A2178, 2017 | 69 | 2017 |
| Geometric subspace updates with applications to online adaptive nonlinear model reduction R Zimmermann, B Peherstorfer, K Willcox SIAM Journal on Matrix Analysis and Applications 39 (1), 234-261, 2018 | 61 | 2018 |
| Multifidelity Monte Carlo estimation of variance and sensitivity indices E Qian, B Peherstorfer, D O'Malley, VV Vesselinov, K Willcox SIAM/ASA Journal on Uncertainty Quantification 6 (2), 683-706, 2018 | 59 | 2018 |
| Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points B Peherstorfer, Z Drmač, S Gugercin SIAM Journal on Scientific Computing 42 (5), A2837-A2864, 2020 | 56 | 2020 |
| Density Estimation with Adaptive Sparse Grids for Large Data Sets B Peherstorfer, D Pflüge, HJ Bungartz SIAM Data Mining 2014, 2014 | 48 | 2014 |
| Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models B Peherstorfer, B Kramer, K Willcox Journal of Computational Physics, 2017 | 46 | 2017 |
| Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation B Peherstorfer, B Kramer, K Willcox SIAM/ASA Journal on Uncertainty Quantification 6 (2), 737-761, 2018 | 45 | 2018 |
Karen WillcoxOden Institute for Computational Engineering and Sciences, UT AustinVerified email at oden.utexas.edu
