| A SURVEY OF PROJECTION-BASED MODEL REDUCTION METHODS FOR PARAMETRIC DYNAMICAL SYSTEMS P BENNER, S GUGERCIN, K WILLCOX SIAM Review 57 (4), 483–531, 2015 | 2079 | 2015 |
| Balanced model reduction via the proper orthogonal decomposition K Willcox, J Peraire AIAA Journal 40 (11), 2323-2330, 2002 | 1299 | 2002 |
| Survey of multifidelity methods in uncertainty propagation, inference, and optimization B Peherstorfer, K Willcox, M Gunzburger SIAM Review 60 (3), 550-591, 2018 | 1146 | 2018 |
| Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition T Bui-Thanh, M Damodaran, KE Willcox AIAA Journal 42 (8), 1505-1516, 2004 | 594 | 2004 |
| Missing point estimation in models described by proper orthogonal decomposition P Astrid, S Weiland, K Willcox, T Backx IEEE Transactions on Automatic Control 53 (10), 2237-2251, 2008 | 579 | 2008 |
| Model Reduction and Approximation: Theory and Algorithms P Benner, M Ohlberger, A Cohen, K Willcox Society for Industrial and Applied Mathematics, 2017 | 575 | 2017 |
| Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition K Willcox Computers & fluids 35 (2), 208-226, 2006 | 517 | 2006 |
| Model reduction for large-scale systems with high-dimensional parametric input space T Bui-Thanh, K Willcox, O Ghattas SIAM Journal on Scientific Computing 30 (6), 3270-3288, 2008 | 493 | 2008 |
| 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 | 464 | 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 | 387 | 2019 |
| Optimal model management for multifidelity Monte Carlo estimation B Peherstorfer, K Willcox, M Gunzburger SIAM Journal on Scientific Computing 38 (5), A3163-A3194, 2016 | 322 | 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 | 316 | 2020 |
| Parameter and state model reduction for large-scale statistical inverse problems C Lieberman, K Willcox, O Ghattas SIAM Journal on Scientific Computing 32 (5), 2523-2542, 2010 | 314 | 2010 |
| Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics T Bui-Thanh, M Damodaran, K Willcox 21st AIAA Applied Aerodynamics Conference, 4213, 2003 | 314 | 2003 |
| Proper orthogonal decomposition extensions for parametric applications in transonic aerodynamics T Bui-Thanh, M Damodaran, K Willcox 21th AIAA Applied Aerodynamics Conference, AIAA Paper 2003-4213, 2003 | 314* | 2003 |
| Surrogate-based optimization using multifidelity models with variable parameterization and corrected space mapping TD Robinson, MS Eldred, KE Willcox, R Haimes Aiaa Journal 46 (11), 2814-2822, 2008 | 295 | 2008 |
| Non-linear model reduction for uncertainty quantification in large-scale inverse problems D Galbally, K Fidkowski, K Willcox, O Ghattas John Wiley & Sons, Ltd., 2010 | 292 | 2010 |
| Localized discrete empirical interpolation method B Peherstorfer, D Butnaru, K Willcox, HJ Bungartz SIAM Journal on Scientific Computing 36 (1), A168-A192, 2014 | 290 | 2014 |
| A probabilistic graphical model foundation for enabling predictive digital twins at scale MG Kapteyn, JVR Pretorius, KE Willcox Nature Computational Science 1 (5), 337-347, 2021 | 255 | 2021 |
| Multifidelity approaches for optimization under uncertainty LWT Ng, KE Willcox International Journal for Numerical Methods in Engineering 100 (10), 746-772, 2014 | 251 | 2014 |
Benjamin PeherstorferCourant Institute of Mathematical Sciences, New York UniversityVerified email at cims.nyu.edu
