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Kutz, J. Nathan

  1. Data-driven discovery of partial differential equations
    2016/09/21 by Rudy, Samuel H., Brunton, Steven L., Proctor, Joshua L. +1 · 55 citations
    #FOS: Physical sciences #Pattern Formation and Solitons (nlin.PS)
  2. Modern Koopman Theory for Dynamical Systems
    2021/02/24 by Steven L. Brunton, Marko Budišić, Brunton, Steven L. +5 · 71 citations
    Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Fluid Dynamics and Turbulent Flows #Power System Optimization and Stability
  3. Dynamic mode decomposition with control
    2014/09/22 by Joshua L. Proctor, Proctor, Joshua L., Steven L. Brunton +3 · 36 citations
    Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Machine Fault Diagnosis Techniques #Structural Health Monitoring Techniques
  4. PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
    2020/04/17 by Brian M. de Silva, de Silva, Brian M., Kathleen Champion +9 · 1 voice · 7 citations
    Computer Science · Engineering · Mathematics · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #math.DS #physics.comp-ph
  5. Generalizing Koopman Theory to allow for inputs and control
    2016/02/24 by Proctor, Joshua L., Brunton, Steven L., Kutz, J. Nathan · 16 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  6. Sparse Identification of Nonlinear Dynamics with Control (SINDYc)
    2016/05/21 by Brunton, Steven L., Proctor, Joshua L., Kutz, J. Nathan · 16 citations
    #37-XX #37B55 #37N10 #37N35 #93-XX #93B30 #Dynamical Systems (math.DS) #FOS: Mathematics
  7. Data-driven discovery of Koopman eigenfunctions for control
    2017/07/04 by Kaiser, Eurika, Kutz, J. Nathan, Brunton, Steven L. · 15 citations
    #Dynamical Systems (math.DS) #FOS: Mathematics #Optimization and Control (math.OC)
  8. A unified sparse optimization framework to learn parsimonious\n physics-informed models from data
    2019/06/25 by Kathleen M. Champion, Champion, Kathleen, Peng Zheng +7 · 16 citations
    Computer Science · Materials Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning in Materials Science #Model Reduction and Neural Networks
  9. Inferring biological networks by sparse identification of nonlinear dynamics
    2016/05/26 by Mangan, Niall M., Brunton, Steven L., Proctor, Joshua L. +1 · 12 citations
    #Dynamical Systems (math.DS) #FOS: Mathematics
  10. Data-driven identification of parametric partial differential equations
    2018/06/03 by Rudy, Samuel, Alla, Alessandro, Brunton, Steven L. +1 · 9 citations
    #FOS: Mathematics #Numerical Analysis (math.NA)
  11. A Unified Framework for Sparse Relaxed Regularized Regression: SR3
    2018/07/14 by Peng Zheng, Zheng, Peng, Travis Askham +7 · 9 citations
    Engineering · Mathematics · #49M15 #62F35 #65K10 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microwave Imaging and Scattering Analysis #Numerical methods in inverse problems #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques
  12. DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems
    2020/12/31 by Gin, Craig R., Shea, Daniel E., Brunton, Steven L. +1 · 9 citations
    #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Numerical Analysis (math.NA)
  13. Multi-Resolution Dynamic Mode Decomposition
    2015/06/01 by J. Nathan Kutz, Kutz, J. Nathan, Xing Fu +3 · 7 citations
    Earth and Planetary Sciences · Engineering · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Mathematics #Machine Fault Diagnosis Techniques #Model Reduction and Neural Networks #Seismic Imaging and Inversion Techniques
  14. SINDy with Control: A Tutorial
    2021/08/30 by Urban Fasel, Eurika Kaiser, Fasel, Urban +7 · 8 citations
    Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Control Systems and Identification #Fault Detection and Control Systems
  15. Variable projection methods for an optimized dynamic mode decomposition
    2017/04/01 by Travis Askham, Askham, Travis, J. Nathan Kutz +1 · 7 citations
    Decision Sciences · Engineering · #37M02 #49M02 #65P02 #Control Systems and Identification #FOS: Mathematics #Fault Detection and Control Systems #Numerical Analysis (math.NA) #Scientific Measurement and Uncertainty Evaluation
  16. Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
    2022/01/13 by Joseph Bakarji, Kathleen M. Champion, Bakarji, Joseph +5 · 8 citations
    Computer Science · Physics and Astronomy · #Computational Engineering #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Finance #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Time Series Analysis and Forecasting #and Science (cs.CE)
  17. Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
    2020/09/12 by Kadierdan Kaheman, Kaheman, Kadierdan, Steven L. Brunton +3 · 7 citations
    Computer Science · Engineering · #93B30 #Anomaly Detection Techniques and Applications #Control Systems and Identification #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Time Series Analysis and Forecasting #electronic engineering #information engineering
  18. Sensing with shallow recurrent decoder networks
    2023/01/27 by Williams, Jan P., Zahn, Olivia, Kutz, J. Nathan · 11 citations
    #Dynamical Systems (math.DS) #FOS: Mathematics
  19. Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
    2022/04/07 by Pan, Shaowu, Brunton, Steven L., Kutz, J. Nathan · 8 citations
    #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)
  20. Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery
    2021/07/05 by Seth M. Hirsh, David A. Barajas‐Solano, Hirsh, Seth M. +3 · 7 citations
    Physics and Astronomy · Computer Science · Engineering · #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference #Control Systems and Identification
  21. Dynamic Mode Decomposition for Financial Trading Strategies
    2015/08/18 by Mann, Jordan, Kutz, J. Nathan · 5 citations
    #Computational Finance (q-fin.CP) #FOS: Economics and business
  22. Time-Delay Observables for Koopman: Theory and Applications
    2018/09/24 by Kamb, Mason, Kaiser, Eurika, Brunton, Steven L. +1 · 5 citations
    #Dynamical Systems (math.DS) #FOS: Mathematics #Numerical Analysis (math.NA)
  23. Dimensionally Consistent Learning with Buckingham Pi
    2022/02/09 by Bakarji, Joseph, Callaham, Jared, Brunton, Steven L. +1 · 6 citations
    #Computational Engineering #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)
  24. Data-Driven Aerospace Engineering: Reframing the Industry with Machine\n Learning
    2020/08/24 by Steven L. Brunton, J. Nathan Kutz, Brunton, Steven L. +21 · 5 citations
    Computer Science · Engineering · Environmental Science · #Advanced Aircraft Design and Technologies #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Radiative Heat Transfer Studies #Signal Processing (eess.SP) #electronic engineering #information engineering
  25. Optimal Sensor and Actuator Selection using Balanced Model Reduction
    2018/12/04 by Manohar, Krithika, Kutz, J. Nathan, Brunton, Steven L. · 5 citations
    #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
  26. A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
    2023/11/01 by Samuel E. Otto, Nicholas Zolman, Otto, Samuel E. +5 · 7 citations
    Computer Science · Materials Science · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Cell Image Analysis Techniques
  27. Multiresolution Convolutional Autoencoders
    2020/04/10 by Yuying Liu, Colin Ponce, Liu, Yuying +5 · 4 citations
    Earth and Planetary Sciences · Physics and Astronomy · #Cryospheric studies and observations #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Seismic Imaging and Inversion Techniques #electronic engineering #information engineering
  28. Multi-fidelity reduced-order surrogate modeling
    2023/09/01 by Paolo Conti, Conti, Paolo, Mengwu Guo +9 · 7 citations
    Engineering · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Vibration Analysis #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Numerical methods for differential equations
  29. PyDMD: A Python package for robust dynamic mode decomposition
    2024/02/12 by Ichinaga, Sara M., Andreuzzi, Francesco, Demo, Nicola +5 · 7 citations
    #Computation (stat.CO) #Computational Physics (physics.comp-ph) #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Systems and Control (eess.SY) #electronic engineering #information engineering
  30. Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
    2020/08/22 by Yuying Liu, Liu, Yuying, J. Nathan Kutz +3 · 3 citations
    Computer Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Numerical Analysis (math.NA)
  31. Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects
    2022/03/10 by Megan R. Ebers, Katherine M. Steele, Ebers, Megan R. +3 · 4 citations
    Engineering · Physics and Astronomy · Decision Sciences · #Fault Detection and Control Systems #Model Reduction and Neural Networks #Scientific Measurement and Uncertainty Evaluation
  32. Automating the Practice of Science -- Opportunities, Challenges, and Implications
    2024/08/27 by Musslick, Sebastian, Bartlett, Laura K., Chandramouli, Suyog H. +11 · 6 citations
    #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph)
  33. Learning Discrepancy Models From Experimental Data
    2019/09/18 by Kadierdan Kaheman, Kaheman, Kadierdan, Eurika Kaiser +7 · 3 citations
    Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Hydraulic and Pneumatic Systems #Fault Detection and Control Systems
  34. PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
    2023/06/22 by Shaowu Pan, Eurika Kaiser, Pan, Shaowu +7 · 4 citations
    Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics and Turbulent Flows #Fluid Dynamics and Vibration Analysis #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Systems and Control (eess.SY) #electronic engineering #information engineering
  35. SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
    2024/03/14 by Nicholas Zolman, Zolman, Nicholas, Lagemann, Christian +6 · 5 citations
    Computer Science · #Reinforcement Learning in Robotics #Data Stream Mining Techniques #Anomaly Detection Techniques and Applications
  36. Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery
    2023/01/30 by Liyao Gao, Gao, L. Mars, Urban Fasel +5 · 5 citations
    Chemistry · Computer Science · Engineering · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses
  37. Discovering conservation laws from data for control
    2018/11/02 by Kaiser, Eurika, Kutz, J. Nathan, Brunton, Steven L. · 2 citations
    #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
  38. A toolkit for data-driven discovery of governing equations in high-noise regimes
    2021/11/08 by Charles B. Delahunt, Delahunt, Charles B., J. Nathan Kutz +1 · 3 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #68T05 #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #I.2.6 #J.2 #Machine Learning (cs.LG) #Metabolomics and Mass Spectrometry Studies #Neural Networks and Applications
  39. Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction
    2023/07/20 by Megan R. Ebers, Jan P. Williams, Ebers, Megan R. +5 · 4 citations
    Computer Science · #Target Tracking and Data Fusion in Sensor Networks #Time Series Analysis and Forecasting #Gaussian Processes and Bayesian Inference
  40. Reduced order modeling with shallow recurrent decoder networks
    2025/02/15 by Matteo Tomasetto, Jan P. Williams, Tomasetto, Matteo +7 · 9 citations
    Physics and Astronomy · Computer Science · #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Generative Adversarial Networks and Image Synthesis
  41. Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
    2021/06/09 by Manu Kalia, Kalia, Manu, Steven L. Brunton +7 · 2 citations
    Decision Sciences · Engineering · Physics and Astronomy · #37G05 #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design
  42. HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
    2023/10/07 by Mozes Jacobs, Bingni W. Brunton, Jacobs, Mozes +7 · 3 citations
    Computer Science · Physics and Astronomy · #60H10 (Secondary) #68T07 (Primary) 37H10 #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #I.2 #J.2 #Machine Learning (cs.LG) #Model Reduction and Neural Networks
  43. Numerical differentiation of noisy data: A unifying multi-objective optimization framework
    2020/09/03 by van Breugel, Floris, Kutz, J. Nathan, Brunton, Bingni W. · 2 citations
    #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Signal Processing (eess.SP) #electronic engineering #information engineering
  44. Data-driven discovery of governing equations for coarse-grained heterogeneous network dynamics
    2022/05/23 by Katherine Owens, Owens, Katherine, J. Nathan Kutz +1 · 2 citations
    Computer Science · Engineering · #34 #37 #Dynamical Systems (math.DS) #FOS: Mathematics #Nonlinear Dynamics and Pattern Formation #Slime Mold and Myxomycetes Research
  45. The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control
    2022/05/12 by Kadierdan Kaheman, Urban Fasel, Kaheman, Kadierdan +9 · 2 citations
    Physics and Astronomy · #Experimental and Theoretical Physics Studies
  46. Multi-fidelity sensor selection: Greedy algorithms to place cheap and\n expensive sensors with cost constraints
    2020/05/07 by Emily Clark, Steven L. Brunton, Clark, Emily +3 · 2 citations
    Decision Sciences · Engineering · #Advanced Statistical Process Monitoring #FOS: Electrical engineering #Fault Detection and Control Systems #Scientific Measurement and Uncertainty Evaluation #Signal Processing (eess.SP) #electronic engineering #information engineering
  47. Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video
    2014/04/30 by Grosek, Jacob, Kutz, J. Nathan · 1 citation
    #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
  48. Statistical Mechanics of Dynamical System Identification
    2024/03/04 by Andrei A. Klishin, Joseph Bakarji, Klishin, Andrei A. +5 · 3 citations
    Engineering · Computer Science · #Control Systems and Identification #Neural Networks and Applications #Fault Detection and Control Systems
  49. Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors
    2024/09/19 by Stefano Riva, Riva, Stefano, Carolina Introini +5 · 4 citations
    Engineering · #Computational Physics (physics.comp-ph) #FOS: Mathematics #FOS: Physical sciences #Fault Detection and Control Systems #Instrumentation and Detectors (physics.ins-det) #Nuclear Engineering Thermal-Hydraulics #Nuclear reactor physics and engineering #Numerical Analysis (math.NA)
  50. Nonlinear parametric models of viscoelastic fluid flows
    2023/08/08 by Cássio M. Oishi, Oishi, Cassio M., Alan A. Kaptanoglu +5 · 3 citations
    Chemical Engineering · Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Rheology and Fluid Dynamics Studies
  51. Online interpolation point refinement for reduced order models using a genetic algorithm
    2016/07/24 by Sargsyan, Syuzanna, Brunton, Steven L., Kutz, J. Nathan · 1 citation
    #FOS: Mathematics #FOS: Physical sciences #Numerical Analysis (math.NA) #Pattern Formation and Solitons (nlin.PS)
  52. PySensors 2.0: A Python Package for Sparse Sensor Placement
    2021/02/16 by Karnik, Niharika, de Silva, Brian M., Bhangale, Yash +12 · 3 citations
    Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms
  53. Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems
    2024/10/02 by Voina, Doris, Brunton, Steven, Kutz, J. Nathan · 3 citations
    #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)
  54. Greedy Sensor Placement with Cost Constraints
    2018/05/09 by Clark, Emily, Askham, Travis, Brunton, Steven L. +1 · 1 citation
    #FOS: Mathematics #Optimization and Control (math.OC)
  55. Data-driven approximations of dynamical systems operators for control
    2019/02/26 by Kaiser, Eurika, Kutz, J. Nathan, Brunton, Steven L. · 1 citation
    #Data Analysis #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC) #Statistics and Probability (physics.data-an)
  56. Nonlinear control in the nematode C. elegans
    2020/01/23 by Morrison, Megan, Fieseler, Charles, Kutz, J. Nathan · 1 citation
    #92B05 #Biological Physics (physics.bio-ph) #Dynamical Systems (math.DS) #FOS: Biological sciences #FOS: Mathematics #FOS: Physical sciences #J.3 #Neurons and Cognition (q-bio.NC)
  57. Sensor Selection With Cost Constraints for Dynamically Relevant Bases
    2020/03/17 by Clark, Emily, Kutz, J. Nathan, Brunton, Steven L. · 1 citation
    #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Signal Processing (eess.SP) #electronic engineering #information engineering
  58. Principal component trajectories for modeling spectrally-continuous dynamics as forced linear systems
    2020/05/28 by Daniel Dylewsky, Dylewsky, Daniel, Eurika Kaiser +5 · 1 citation
    Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Energy Load and Power Forecasting #FOS: Electrical engineering #FOS: Physical sciences #Model Reduction and Neural Networks #Power System Optimization and Stability #Systems and Control (eess.SY) #electronic engineering #information engineering
  59. Deep reinforcement learning for optical systems: A case study of mode-locked lasers
    2020/06/10 by Chang Sun, Eurika Kaiser, Sun, Chang +5 · 1 citation
    Physics and Astronomy · Computer Science · Engineering · #Advanced Fiber Laser Technologies #Neural Networks and Reservoir Computing #Semiconductor Lasers and Optical Devices
  60. Nonlinear control of networked dynamical systems
    2020/06/09 by Morrison, Megan, Kutz, J. Nathan · 1 citation
    #37G99 (Secondary) #93C10 (Primary) 34H20 #Adaptation and Self-Organizing Systems (nlin.AO) #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences
  61. Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
    2025/01/23 by Gao, Mars Liyao, Williams, Jan P., Kutz, J. Nathan · 5 citations
    #Artificial Intelligence (cs.AI) #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG)
  62. Robust Trimmed k-means
    2021/08/16 by Dorabiala, Olga, Kutz, J. Nathan, Aravkin, Aleksandr · 1 citation
    #62F35 #90C26 #FOS: Computer and information sciences #FOS: Mathematics #I.5.3 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  63. Robust and scalable methods for the dynamic mode decomposition
    2017/12/05 by Askham, Travis, Zheng, Peng, Aravkin, Aleksandr +1 · 1 citation
    #Dynamical Systems (math.DS) #FOS: Mathematics #Optimization and Control (math.OC)
  64. Data-driven sensor placement with shallow decoder networks
    2022/02/10 by Jan M. Williams, Williams, Jan, Olivia Zahn +3 · 1 citation
    Computer Science · Engineering · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Mathematics #Model Reduction and Neural Networks #Sparse and Compressive Sensing Techniques #Target Tracking and Data Fusion in Sensor Networks
  65. Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics
    2024/03/03 by Faraji, Farbod, Reza, Maryam, Knoll, Aaron +1 · 2 citations
    #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Plasma Physics (physics.plasm-ph)
  66. The Adaptive Spectral Koopman Method for Dynamical Systems
    2022/02/19 by Bian Li, Li, Bian, Yi-An Ma +5 · 1 citation
    Engineering · Mathematics · Physics and Astronomy · #37L65 (primary) 65L60 (secondary) #Dynamical Systems (math.DS) #FOS: Mathematics #Fractional Differential Equations Solutions #Lattice Boltzmann Simulation Studies #Model Reduction and Neural Networks
  67. Saddle Transport and Chaos in the Double Pendulum
    2022/09/21 by Kadierdan Kaheman, Jason J. Bramburger, Kaheman, Kadierdan +5 · 1 citation
    Engineering · Physics and Astronomy · #37J46 #65P99 #Astro and Planetary Science #Dynamical Systems (math.DS) #FOS: Mathematics #Solar and Space Plasma Dynamics #Spacecraft Dynamics and Control
  68. Pilot-Wave Dynamics: Using Dynamic Mode Decomposition to characterize Bifurcations, Routes to Chaos and Emergent Statistics
    2022/10/13 by Kutz, J. Nathan, Nachbin, Andre, Baddoo, Peter J. +1 · 1 citation
    #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Pattern Formation and Solitons (nlin.PS)
  69. From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
    2025/03/11 by Introini, Carolina, Riva, Stefano, Kutz, J. Nathan +1 · 3 citations
    #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Learning (cs.LG)
  70. Extremum seeking control of quantum gates
    2023/09/08 by Abbasgholinejad, Erfan, Deng, Haoqin, Gamble, John +4 · 1 citation
    #FOS: Physical sciences #Quantum Physics (quant-ph)
  71. Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns
    2023/08/26 by Faraji, Farbod, Reza, Maryam, Knoll, Aaron +1 · 1 citation
    #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Plasma Physics (physics.plasm-ph)
  72. Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: II. dynamics forecasting
    2023/08/26 by Faraji, Farbod, Reza, Maryam, Knoll, Aaron +1 · 1 citation
    #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Plasma Physics (physics.plasm-ph)
  73. Ensemble Principal Component Analysis
    2023/11/03 by Dorabiala, Olga, Aravkin, Aleksandr, Kutz, J. Nathan · 1 citation
    #62F40 #62H30 #Computation (stat.CO) #FOS: Computer and information sciences #I.5.3 #I.5.4
  74. Long Sequence Decoder Network for Mobile Sensing
    2024/07/14 by Mei, Jiazhong, Kutz, J. Nathan · 1 citation
    #Dynamical Systems (math.DS) #FOS: Mathematics
  75. Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics
    2024/05/20 by J. Nathan Kutz, Kutz, J. Nathan, Maryam Reza +5 · 1 citation
    Engineering · Mathematics · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Numerical methods for differential equations #Particle accelerators and beam dynamics #Pattern Formation and Solitons (nlin.PS) #Plasma Diagnostics and Applications #Plasma Physics (physics.plasm-ph)
  76. VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
    2024/05/31 by Conti, Paolo, Kneifl, Jonas, Manzoni, Andrea +4 · 1 citation
    #Computational Engineering #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Finance #Machine Learning (cs.LG) #and Science (cs.CE)
  77. Universal Dynamics of Damped-Driven Systems: The Logistic Map as a Normal Form for Energy Balance
    2022/11/22 by Kutz, J. Nathan, Rahman, Aminur, Ebers, Megan R. +2 · 1 citation
    #Adaptation and Self-Organizing Systems (nlin.AO) #Chaotic Dynamics (nlin.CD) #Data Analysis #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences #Popular Physics (physics.pop-ph) #Statistics and Probability (physics.data-an)
  78. Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
    2022/11/19 by Liyao Gao, J. Nathan Kutz, Gao, L. Mars +1 · 1 citation
    Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gamma-ray bursts and supernovae #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
  79. Reservoir computing for system identification and predictive control with limited data
    2024/10/23 by Williams, Jan P., J. Nathan Kutz, Kutz, J. Nathan +2 · 1 citation
    Engineering · Computer Science · #Reservoir Engineering and Simulation Methods #Neural Networks and Applications #Advanced Control Systems Optimization
  80. PySensors 2.0: A Python Package for Sparse Sensor Placement
    2025/09/09 by Karnik, Niharika, Bhangale, Yash, Abdo, Mohammad G. +6 · 1 citation
    #FOS: Computer and information sciences #Robotics (cs.RO)
  81. Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC
    2022/09/20 by Andrea Tagliabue, Yi‐Hsuan Hsiao, Tagliabue, Andrea +11 · 1 citation
    Computer Science · Engineering · #Adaptive Control of Nonlinear Systems #Biomimetic flight and propulsion mechanisms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotic Path Planning Algorithms #Robotics (cs.RO)
  82. Data-Induced Interactions of Sparse Sensors Using Statistical Physics
    2023/07/21 by Andrei A. Klishin, Klishin, Andrei A., J. Nathan Kutz +3 · 2 citations
    Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Statistical Mechanics (cond-mat.stat-mech) #Statistical Mechanics and Entropy #Theoretical and Computational Physics #electronic engineering #information engineering