| Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems | 0 | 0.34 | 2022 |
| Robust and Scalable Methods for the Dynamic Mode Decomposition* | 0 | 0.34 | 2022 |
| Modern Koopman Theory for Dynamical Systems | 0 | 0.34 | 2022 |
| Optimal Sensor and Actuator Selection Using Balanced Model Reduction | 0 | 0.34 | 2022 |
| Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data | 0 | 0.34 | 2022 |
| Deeptime: a Python library for machine learning dynamical models from time series data | 0 | 0.34 | 2022 |
| A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes | 0 | 0.34 | 2022 |
| Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. | 0 | 0.34 | 2022 |
| SINDy with Control: A Tutorial | 0 | 0.34 | 2021 |
| Nonlinear Control of Networked Dynamical Systems | 0 | 0.34 | 2021 |
| Centering Data Improves the Dynamic Mode Decomposition | 0 | 0.34 | 2020 |
| Deep reinforcement learning for optical systems: A case study of mode-locked lasers. | 0 | 0.34 | 2020 |
| Sparse Principal Component Analysis via Variable Projection. | 1 | 0.35 | 2020 |
| Discovery of Physics From Data: Universal Laws and Discrepancies. | 2 | 0.48 | 2020 |
| A Unified Framework for Sparse Relaxed Regularized Regression: SR3. | 0 | 0.34 | 2019 |
| Compressed dynamic mode decomposition for background modeling | 3 | 0.40 | 2019 |
| Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings | 1 | 0.38 | 2019 |
| Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. | 0 | 0.34 | 2019 |
| Optimized Sampling for Multiscale Dynamics. | 0 | 0.34 | 2019 |
| Randomized Dynamic Mode Decomposition | 0 | 0.34 | 2019 |
| Smoothing and parameter estimation by soft-adherence to governing equations. | 0 | 0.34 | 2019 |
| Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. | 0 | 0.34 | 2019 |
| Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries. | 0 | 0.34 | 2019 |
| A unified sparse optimization framework to learn parsimonious physics-informed models from data. | 0 | 0.34 | 2019 |
| Discovery of Physics from Data: Universal Laws and Discrepancy Models. | 0 | 0.34 | 2019 |
| Shape Constrained Tensor Decompositions | 0 | 0.34 | 2019 |
| Data-driven multiscale decompositions for forecasting and model discovery. | 0 | 0.34 | 2019 |
| Deep learning of dynamics and signal-noise decomposition with time-stepping constraints. | 5 | 0.44 | 2019 |
| Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition | 1 | 0.36 | 2019 |
| Engineering structural robustness in power grid networks susceptible to community desynchronization. | 0 | 0.34 | 2019 |
| Complex Algorithms for Data-Driven Model Learning in Science and Engineering. | 0 | 0.34 | 2019 |
| Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. | 1 | 0.35 | 2019 |
| Deep Model Predictive Control with Online Learning for Complex Physical Systems. | 0 | 0.34 | 2019 |
| Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm. | 0 | 0.34 | 2018 |
| Optimal Sensor and Actuator Placement using Balanced Model Reduction. | 0 | 0.34 | 2018 |
| Generalizing Koopman Theory to Allow for Inputs and Control. | 11 | 0.96 | 2018 |
| Discovering Conservation Laws From Data For Control | 0 | 0.34 | 2018 |
| Sparse Relaxed Regularized Regression: SR3. | 0 | 0.34 | 2018 |
| Putting a bug in ML: The moth olfactory network learns to read MNIST. | 1 | 0.35 | 2018 |
| A moth brain learns to read MNIST. | 0 | 0.34 | 2018 |
| Diffusion Maps meet Nyström. | 0 | 0.34 | 2018 |
| Sparse Principal Component Analysis via Variable Projection. | 1 | 0.43 | 2018 |
| Variable Projection Methods for an Optimized Dynamic Mode Decomposition. | 3 | 0.46 | 2018 |
| Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data. | 0 | 0.34 | 2018 |
| Sparsity enabled cluster reduced-order models for control. | 2 | 0.42 | 2018 |
| Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems. | 0 | 0.34 | 2018 |
| Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets. | 1 | 0.41 | 2018 |
| Randomized Nonnegative Matrix Factorization. | 3 | 0.38 | 2018 |
| Machine Learning And Air Quality Modeling | 0 | 0.34 | 2017 |
| Nonlinear Model Order Reduction via Dynamic Mode Decomposition. | 6 | 0.47 | 2017 |
