| Linearly recurrent autoencoder networks for learning dynamics SE Otto, CW Rowley SIAM Journal on Applied Dynamical Systems 18 (1), 558-593, 2019 | 326 | 2019 |
| Koopman operators for estimation and control of dynamical systems SE Otto, CW Rowley Annual Review of Control, Robotics, and Autonomous Systems 4, 59-87, 2021 | 109 | 2021 |
| Data-driven model predictive control using interpolated Koopman generators S Peitz, SE Otto, CW Rowley SIAM Journal on Applied Dynamical Systems 19 (3), 2162-2193, 2020 | 77 | 2020 |
| Distortion correction protocol for digital image correlation after scanning electron microscopy: emphasis on long duration and ex-situ experiments AW Mello, TA Book, A Nicolas, SE Otto, CJ Gilpin, MD Sangid Experimental Mechanics 57, 1395-1409, 2017 | 52 | 2017 |
| Analysis of amplification mechanisms and cross-frequency interactions in nonlinear flows via the harmonic resolvent A Padovan, SE Otto, CW Rowley Journal of Fluid Mechanics 900, A14, 2020 | 37 | 2020 |
| Inward-turning streamline-traced inlet design method for low-boom, low-drag applications SE Otto, CJ Trefny, JW Slater Journal of Propulsion and Power 32 (5), 1178-1189, 2016 | 36 | 2016 |
| Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems: a new approach using secants SE Otto, CW Rowley Journal of Nonlinear Science 32 (5), 69, 2022 | 12 | 2022 |
| Optimizing oblique projections for nonlinear systems using trajectories SE Otto, A Padovan, CW Rowley SIAM Journal on Scientific Computing 44 (3), A1681-A1702, 2022 | 8 | 2022 |
| Model reduction for nonlinear systems by balanced truncation of state and gradient covariance SE Otto, A Padovan, CW Rowley SIAM Journal on Scientific Computing 45 (5), A2325-A2355, 2023 | 7 | 2023 |
| Learning Bilinear Models of Actuated Koopman Generators from Partially Observed Trajectories S Otto, S Peitz, C Rowley SIAM Journal on Applied Dynamical Systems 23 (1), 885-923, 2024 | 6 | 2024 |
| A unified framework to enforce, discover, and promote symmetry in machine learning SE Otto, N Zolman, JN Kutz, SL Brunton arXiv preprint arXiv:2311.00212, 2023 | 5 | 2023 |
| A discrete empirical interpolation method for interpretable immersion and embedding of nonlinear manifolds SE Otto, CW Rowley arXiv preprint arXiv:1905.07619, 2019 | 4 | 2019 |
| Learning nonlinear projections for reduced-order modeling of dynamical systems using constrained autoencoders SE Otto, GR Macchio, CW Rowley Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (11), 2023 | 3 | 2023 |
| Advances in data-driven modeling and sensing for high-dimensional nonlinear systems SE Otto Princeton University, 2022 | 3 | 2022 |
| Operator learning without the adjoint N Boullé, D Halikias, SE Otto, A Townsend arXiv preprint arXiv:2401.17739, 2024 | 1 | 2024 |
| Inward-turning streamline-traced supersonic inlet design method for low-boom, low-drag applications SE Otto, CJ Trefny, JW Slater 51st AIAA/SAE/ASEE Joint Propulsion Conference, 3700, 2015 | 1 | 2015 |
| Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding SE Otto, CM Oishi, F Amaral, SL Brunton, JN Kutz arXiv preprint arXiv:2404.14347, 2024 | | 2024 |
| On the role of the projection fiber for modeling transient nonlinear dynamics S Otto, N Kutz, S Brunton Bulletin of the American Physical Society, 2023 | | 2023 |
| Nonlinear Oblique Projections for Reduced-Order Modeling using Constrained Autoencoders G Macchio, S Otto, C Rowley Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Leveraging Dynamics for Near-Optimal, Ultra-Sparse Sensor Placement S Otto, C Rowley APS Division of Fluid Dynamics Meeting Abstracts, P17. 004, 2019 | | 2019 |
