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Samuel E. Otto
Samuel E. Otto
Sibley School of Mechanical and Aerospace Engineering, Cornell University
Verified email at cornell.edu
Title
Cited by
Cited by
Year
Linearly recurrent autoencoder networks for learning dynamics
SE Otto, CW Rowley
SIAM Journal on Applied Dynamical Systems 18 (1), 558-593, 2019
3862019
Koopman operators for estimation and control of dynamical systems
SE Otto, CW Rowley
Annual Review of Control, Robotics, and Autonomous Systems 4 (1), 59-87, 2021
1612021
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
1102020
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
622017
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
512020
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
392016
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
162024
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
162023
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
152022
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
132023
Optimizing oblique projections for nonlinear systems using trajectories
SE Otto, A Padovan, CW Rowley
SIAM Journal on Scientific Computing 44 (3), A1681-A1702, 2022
132022
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
112023
A discrete empirical interpolation method for interpretable immersion and embedding of nonlinear manifolds
SE Otto, CW Rowley
arXiv preprint arXiv:1905.07619, 2019
52019
Advances in data-driven modeling and sensing for high-dimensional nonlinear systems
SE Otto
Princeton University, 2022
42022
Operator learning without the adjoint
N Boullé, D Halikias, SE Otto, A Townsend
arXiv preprint arXiv:2401.17739, 2024
32024
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
12015
Data-Driven Dimension Reduction Through Symmetry-Promoting Regularization
N Zolman, S Otto, JN Kutz, S Brunton
Bulletin of the American Physical Society, 2024
2024
Machine learning in viscoelastic fluids via energy-based kernel embedding
SE Otto, CM Oishi, FVG Amaral, SL Brunton, JN Kutz
Journal of Computational Physics 516, 113371, 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
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