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2020 – today
- 2024
[j100]Olga Dorabiala
, Aleksandr Y. Aravkin, J. Nathan Kutz:
Ensemble Principal Component Analysis. IEEE Access 12: 6663-6671 (2024)- [j99]Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz:
Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction. IEEE Access 12: 97428-97439 (2024) - [j98]Jacob Stevens-Haas, Yash Bhangale, J. Nathan Kutz, Aleksandr Y. Aravkin:
Learning Nonlinear Dynamics Using Kalman Smoothing. IEEE Access 12: 138564-138574 (2024) - [j97]J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li:
AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control. AI Mag. 45(1): 48-53 (2024) - [j96]Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton:
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. J. Open Source Softw. 9(96): 5881 (2024) - [j95]Steven L. Brunton, J. Nathan Kutz:
Promising directions of machine learning for partial differential equations. Nat. Comput. Sci. 4(7): 483-494 (2024) - [j94]Jiazhong Mei, Steven L. Brunton, J. Nathan Kutz:
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. Sensors 24(12): 3727 (2024) - [j93]Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz:
Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects. SIAM J. Appl. Dyn. Syst. 23(1): 440-469 (2024) - [j92]Megan Morrison, J. Nathan Kutz:
Solving Nonlinear Ordinary Differential Equations Using the Invariant Manifolds and Koopman Eigenfunctions. SIAM J. Appl. Dyn. Syst. 23(1): 924-960 (2024) - [j91]Aleksei Sholokhov, Saleh Nabi, Joshua Rapp, Steven L. Brunton, J. Nathan Kutz, Petros T. Boufounos, Hassan Mansour:
Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics. IEEE Trans. Computational Imaging 10: 1124-1138 (2024) - [c14]Aleksei Sholokhov, Joshua Rapp, Saleh Nabi, Steven L. Brunton, J. Nathan Kutz, Hassan Mansour:
Single-Pixel Imaging Of Dynamic Flows Using Neural Ode Regularization. ICASSP 2024: 2530-2534 - [i96]Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz:
PyDMD: A Python package for robust dynamic mode decomposition. CoRR abs/2402.07463 (2024) - [i95]Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz:
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks. CoRR abs/2402.09234 (2024) - [i94]Olivia T. Zahn, Thomas L. Daniel, J. Nathan Kutz:
Motif distribution and function of sparse deep neural networks. CoRR abs/2403.00974 (2024) - [i93]Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz:
Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications. CoRR abs/2403.01523 (2024) - [i92]Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz:
Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics. CoRR abs/2403.01532 (2024) - [i91]Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar:
Statistical Mechanics of Dynamical System Identification. CoRR abs/2403.01723 (2024) - [i90]Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton:
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. CoRR abs/2403.09110 (2024) - [i89]Meghana Velegar, Christoph Keller, J. Nathan Kutz:
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data. CoRR abs/2404.12396 (2024) - [i88]J. Nathan Kutz, Maryam Reza, Farbod Faraji, Aaron Knoll:
Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics. CoRR abs/2405.11955 (2024) - [i87]Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz:
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. CoRR abs/2405.20905 (2024) - [i86]Sebastian Musslick, Laura Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes:
Automating the Practice of Science - Opportunities, Challenges, and Implications. CoRR abs/2409.05890 (2024) - [i85]Stefano Riva, Carolina Introini, Antonio Cammi, J. Nathan Kutz:
Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. CoRR abs/2409.12550 (2024) - [i84]Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Bernd Bewer, Sophie Jentzsch, Tobias Hecking, J. Nathan Kutz:
AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues. CoRR abs/2409.14206 (2024) - [i83]Doris Voina, Steven L. Brunton, J. Nathan Kutz:
Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems. CoRR abs/2410.02079 (2024) - [i82]Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Wafa Sadri, Carsten Hartmann, Tobias Hecking, J. Nathan Kutz:
Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight. CoRR abs/2410.16397 (2024) - 2023
- [j90]John Ferré, Ariel Rokem, Elizabeth A. Buffalo, J. Nathan Kutz, Adrienne Fairhall:
Non-Stationary Dynamic Mode Decomposition. IEEE Access 11: 117159-117176 (2023) - [j89]Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz:
Multiresolution convolutional autoencoders. J. Comput. Phys. 474: 111801 (2023) - [j88]Shaowu Pan, Steven L. Brunton, J. Nathan Kutz:
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. J. Mach. Learn. Res. 24: 41:1-41:60 (2023) - [j87]Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman:
Deep learning based object tracking in walking droplet and granular intruder experiments. J. Real Time Image Process. 20(5): 86 (2023) - [j86]Megan Morrison, J. Nathan Kutz, Michael Gabbay:
Transitions between peace and systemic war as bifurcations in a signed network dynamical system. Netw. Sci. 11(3): 458-501 (2023) - [j85]Aminur Rahman, J. Nathan Kutz:
Walking Droplets as a Damped-Driven System. SIAM J. Appl. Dyn. Syst. 22(2): 1219-1233 (2023) - [j84]Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang:
The Adaptive Spectral Koopman Method for Dynamical Systems. SIAM J. Appl. Dyn. Syst. 22(3): 1523-1551 (2023) - [j83]Katherine Owens, J. Nathan Kutz:
Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics. SIAM J. Appl. Dyn. Syst. 22(3): 2601-2623 (2023) - [c13]Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton:
Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. CDC 2023: 4364-4371 - [c12]Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How:
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. ICRA 2023: 3383-3389 - [c11]Erfan Abbasgholinejad, Haoqin Deng, John King Gamble, J. Nathan Kutz, Erik Nielsen, Neal C. Pisenti, Ningzhi Xie:
Extremum Seeking Control of Quantum Gates. QCE 2023: 227-231 - [i81]L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz:
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery. CoRR abs/2301.12649 (2023) - [i80]Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman:
Deep Learning Based Object Tracking in Walking Droplet and Granular Intruder Experiments. CoRR abs/2302.05425 (2023) - [i79]Steven L. Brunton, J. Nathan Kutz:
Machine Learning for Partial Differential Equations. CoRR abs/2303.17078 (2023) - [i78]Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton:
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. CoRR abs/2306.12962 (2023) - [i77]Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz:
Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction. CoRR abs/2307.11793 (2023) - [i76]Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar:
Data-Induced Interactions of Sparse Sensors. CoRR abs/2307.11838 (2023) - [i75]Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton:
Nonlinear parametric models of viscoelastic fluid flows. CoRR abs/2308.04405 (2023) - [i74]Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz:
Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns. CoRR abs/2308.13726 (2023) - [i73]Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz:
Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: II. dynamics forecasting. CoRR abs/2308.13727 (2023) - [i72]Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz:
Multi-fidelity reduced-order surrogate modeling. CoRR abs/2309.00325 (2023) - [i71]Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut:
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations. CoRR abs/2310.04832 (2023) - [i70]Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton:
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. CoRR abs/2311.00212 (2023) - [i69]Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim:
Attention for Causal Relationship Discovery from Biological Neural Dynamics. CoRR abs/2311.06928 (2023) - 2022
- [j82]Charles B. Delahunt, J. Nathan Kutz:
A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes. IEEE Access 10: 31210-31234 (2022) - [j81]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access 10: 33440-33448 (2022) - [j80]Nina de Lacy, Michael J. Ramshaw, J. Nathan Kutz:
Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. Frontiers Artif. Intell. 5: 832530 (2022) - [j79]Alan A. Kaptanoglu, Brian de Silva, Urban Fasel, Kadierdan Kaheman, Andy Goldschmidt, Jared Callaham, Charles B. Delahunt, Zachary Nicolaou, Kathleen P. Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton:
PySINDy: A comprehensive Python package for robust sparse system identification. J. Open Source Softw. 7(69): 3994 (2022) - [j78]Floris van Breugel, Yuying Liu, Bingni W. Brunton, J. Nathan Kutz:
PyNumDiff: A Python package for numerical differentiation of noisy time-series data. J. Open Source Softw. 7(71): 4078 (2022) - [j77]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3(1): 15009 (2022) - [j76]Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz:
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data. Mach. Learn. Sci. Technol. 3(1): 15031 (2022) - [j75]Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz:
Dimensionally consistent learning with Buckingham Pi. Nat. Comput. Sci. 2(12): 834-844 (2022) - [j74]Olivia Zahn, Jorge Bustamante, Callin Switzer, Thomas L. Daniel, J. Nathan Kutz:
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight. PLoS Comput. Biol. 18(9): 1010512 (2022) - [j73]Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin:
Robust trimmed k-means. Pattern Recognit. Lett. 161: 9-16 (2022) - [j72]Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz:
Model predictive control for robust quantum state preparation. Quantum 6: 837 (2022) - [j71]Travis Askham, Peng Zheng, Aleksandr Y. Aravkin, J. Nathan Kutz:
Robust and Scalable Methods for the Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 21(1): 60-79 (2022) - [j70]Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz:
Modern Koopman Theory for Dynamical Systems. SIAM Rev. 64(2): 229-340 (2022) - [j69]Krithika Manohar, J. Nathan Kutz, Steven L. Brunton:
Optimal Sensor and Actuator Selection Using Balanced Model Reduction. IEEE Trans. Autom. Control. 67(4): 2108-2115 (2022) - [i68]Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton:
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. CoRR abs/2201.05136 (2022) - [i67]Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki:
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i66]Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz:
Dimensionally Consistent Learning with Buckingham Pi. CoRR abs/2202.04643 (2022) - [i65]Megan Morrison, J. Nathan Kutz, Michael Gabbay:
Transitions between peace and systemic war as bifurcations in a signed network dynamical system. CoRR abs/2203.04451 (2022) - [i64]Shaowu Pan, Steven L. Brunton, J. Nathan Kutz:
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. CoRR abs/2204.03216 (2022) - [i63]Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton:
The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control. CoRR abs/2205.06231 (2022) - [i62]Alex Mallen, Christoph A. Keller, J. Nathan Kutz:
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data. CoRR abs/2209.08618 (2022) - [i61]Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How:
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. CoRR abs/2209.10007 (2022) - [i60]Olga Dorabiala, Jennifer Webster, J. Nathan Kutz, Aleksandr Y. Aravkin:
Spatiotemporal k-means. CoRR abs/2211.05337 (2022) - [i59]L. Mars Gao, J. Nathan Kutz:
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. CoRR abs/2211.10575 (2022) - 2021
- [j68]Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton:
Data-Driven Stabilization of Periodic Orbits. IEEE Access 9: 43504-43521 (2021) - [j67]Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz:
Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data. IEEE Access 9: 83453-83466 (2021) - [j66]Henning Lange, Steven L. Brunton, J. Nathan Kutz:
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. J. Mach. Learn. Res. 22: 41:1-41:38 (2021) - [j65]Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton:
PySensors: A Python package for sparse sensor placement. J. Open Source Softw. 6(58): 2828 (2021) - [j64]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Data-driven discovery of Koopman eigenfunctions for control. Mach. Learn. Sci. Technol. 2(3): 35023 (2021) - [j63]Megan Morrison, J. Nathan Kutz:
Nonlinear Control of Networked Dynamical Systems. IEEE Trans. Netw. Sci. Eng. 8(1): 174-189 (2021) - [c10]Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton:
SINDy with Control: A Tutorial. CDC 2021: 16-21 - [i58]Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz:
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems. CoRR abs/2101.07206 (2021) - [i57]Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz:
Modern Koopman Theory for Dynamical Systems. CoRR abs/2102.12086 (2021) - [i56]Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz:
PySensors: A Python Package for Sparse Sensor Placement. CoRR abs/2102.13476 (2021) - [i55]Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz:
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data. CoRR abs/2104.01293 (2021) - [i54]Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz:
Deep Learning of Conjugate Mappings. CoRR abs/2104.01874 (2021) - [i53]Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz:
Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations. CoRR abs/2106.05102 (2021) - [i52]Alex Mallen, Henning Lange, J. Nathan Kutz:
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties. CoRR abs/2106.06033 (2021) - [i51]Diya Sashidhar, J. Nathan Kutz:
Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification. CoRR abs/2107.10878 (2021) - [i50]Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin:
Robust Trimmed k-means. CoRR abs/2108.07186 (2021) - [i49]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR abs/2110.15013 (2021) - [i48]Henning Lange, J. Nathan Kutz:
FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics. CoRR abs/2111.00110 (2021) - [i47]Charles B. Delahunt, J. Nathan Kutz:
A toolkit for data-driven discovery of governing equations in high-noise regimes. CoRR abs/2111.04870 (2021) - [i46]Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton:
PySINDy: A comprehensive Python package for robust sparse system identification. CoRR abs/2111.08481 (2021) - [i45]Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton:
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. CoRR abs/2111.10992 (2021) - [i44]Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton:
Physics-informed dynamic mode decomposition (piDMD). CoRR abs/2112.04307 (2021) - 2020
- [j62]Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz:
A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data. IEEE Access 8: 169259-169271 (2020) - [j61]Floris van Breugel, J. Nathan Kutz, Bingni W. Brunton:
Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework. IEEE Access 8: 196865-196877 (2020) - [j60]Megan Morrison, Charles Fieseler, J. Nathan Kutz:
Nonlinear Control in the Nematode C. elegans. Frontiers Comput. Neurosci. 14: 616639 (2020) - [j59]Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz:
Discovery of Physics From Data: Universal Laws and Discrepancies. Frontiers Artif. Intell. 3: 25 (2020) - [j58]Brian de Silva, Kathleen P. Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton:
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. J. Open Source Softw. 5(49): 2104 (2020) - [j57]N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz:
Randomized CP tensor decomposition. Mach. Learn. Sci. Technol. 1(2): 25012 (2020) - [j56]Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz:
Deep reinforcement learning for optical systems: A case study of mode-locked lasers. Mach. Learn. Sci. Technol. 1(4): 45013 (2020) - [j55]Mason Kamb, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz:
Time-Delay Observables for Koopman: Theory and Applications. SIAM J. Appl. Dyn. Syst. 19(2): 886-917 (2020) - [j54]Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz, Bingni W. Brunton:
Centering Data Improves the Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 19(3): 1920-1955 (2020) - [j53]N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
Sparse Principal Component Analysis via Variable Projection. SIAM J. Appl. Math. 80(2): 977-1002 (2020) - [i43]Henning Lange, Steven L. Brunton, J. Nathan Kutz:
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. CoRR abs/2004.00574 (2020) - [i42]Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton:
SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. CoRR abs/2004.02322 (2020) - [i41]Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz:
Multiresolution Convolutional Autoencoders. CoRR abs/2004.04946 (2020) - [i40]Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz:
SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems. CoRR abs/2005.10756 (2020) - [i39]Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz:
Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits. CoRR abs/2005.14321 (2020) - [i38]Jason J. Bramburger, Daniel Dylewsky, J. Nathan Kutz:
Sparse Identification of Slow Timescale Dynamics. CoRR abs/2006.00940 (2020) - [i37]Brian M. de Silva, Jared Callaham, Jonathan Jonker, Nicholas Goebel, Jennifer Klemisch, Darren McDonald, Nathan Hicks, J. Nathan Kutz, Steven L. Brunton, Aleksandr Y. Aravkin:
Physics-informed machine learning for sensor fault detection with flight test data. CoRR abs/2006.13380 (2020) - [i36]Yuying Liu, J. Nathan Kutz, Steven L. Brunton:
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers. CoRR abs/2008.09768 (2020) - [i35]Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas A. Hogan, Darren McDonald:
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. CoRR abs/2008.10740 (2020) - [i34]Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton:
Bracketing brackets with bras and kets. CoRR abs/2008.12247 (2020) - [i33]Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz:
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data. CoRR abs/2009.08810 (2020) - [i32]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR abs/2010.04248 (2020)
2010 – 2019
- 2019
- [j52]Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
A Unified Framework for Sparse Relaxed Regularized Regression: SR3. IEEE Access 7: 1404-1423 (2019) - [j51]Alessandro Alla, J. Nathan Kutz:
Randomized model order reduction. Adv. Comput. Math. 45(3): 1251-1271 (2019) - [j50]Daniel Dylewsky, Xiu Yang, Alexandre M. Tartakovsky, J. Nathan Kutz:
Engineering structural robustness in power grid networks susceptible to community desynchronization. Appl. Netw. Sci. 4(1): 24:1-24:14 (2019) - [j49]Francisco Javier Montáns, Francisco Chinesta, Rafael Gómez-Bombarelli, J. Nathan Kutz:
Complex Algorithms for Data-Driven Model Learning in Science and Engineering. Complex. 2019: 5040637:1-5040637:3 (2019) - [j48]James M. Kunert-Graf, Kristian M. Eschenburg, David J. Galas, J. Nathan Kutz, Swati D. Rane, Bingni W. Brunton:
Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition. Frontiers Comput. Neurosci. 13: 75 (2019) - [j47]Pedro D. Maia, Ashish Raj, J. Nathan Kutz:
Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries. J. Comput. Neurosci. 47(1): 1-16 (2019) - [j46]Samuel H. Rudy, J. Nathan Kutz, Steven L. Brunton:
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints. J. Comput. Phys. 396: 483-506 (2019) - [j45]Samuel H. Rudy, Steven L. Brunton, J. Nathan Kutz:
Smoothing and parameter estimation by soft-adherence to governing equations. J. Comput. Phys. 398 (2019) - [j44]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed dynamic mode decomposition for background modeling. J. Real Time Image Process. 16(5): 1479-1492 (2019) - [j43]Krithika Manohar, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz:
Optimized Sampling for Multiscale Dynamics. Multiscale Model. Simul. 17(1): 117-136 (2019) - [j42]Nina de Lacy, Elizabeth McCauley, J. Nathan Kutz, Vince D. Calhoun:
Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. NeuroImage 202 (2019) - [j41]Charles B. Delahunt, J. Nathan Kutz:
Putting a bug in ML: The moth olfactory network learns to read MNIST. Neural Networks 118: 54-64 (2019) - [j40]Kathleen P. Champion, Steven L. Brunton, J. Nathan Kutz:
Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings. SIAM J. Appl. Dyn. Syst. 18(1): 312-333 (2019) - [j39]Samuel H. Rudy, Alessandro Alla, Steven L. Brunton, J. Nathan Kutz:
Data-Driven Identification of Parametric Partial Differential Equations. SIAM J. Appl. Dyn. Syst. 18(2): 643-660 (2019) - [j38]N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton:
Randomized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 18(4): 1867-1891 (2019) - [c9]Bethany Lusch, Eric C. Chi, J. Nathan Kutz:
Shape Constrained Tensor Decompositions. DSAA 2019: 287-297 - [i31]Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz:
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. CoRR abs/1901.09283 (2019) - [i30]N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz:
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. CoRR abs/1902.07358 (2019) - [i29]Daniel Dylewsky, Molei Tao, J. Nathan Kutz:
Data-driven multiscale decompositions for forecasting and model discovery. CoRR abs/1903.12480 (2019) - [i28]Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz:
Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR abs/1905.10094 (2019) - [i27]Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz:
Discovery of Physics from Data: Universal Laws and Discrepancy Models. CoRR abs/1906.07906 (2019) - [i26]Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz:
A unified sparse optimization framework to learn parsimonious physics-informed models from data. CoRR abs/1906.10612 (2019) - [i25]Kadierdan Kaheman, Eurika Kaiser, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton:
Learning Discrepancy Models From Experimental Data. CoRR abs/1909.08574 (2019) - [i24]Craig Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz:
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs. CoRR abs/1911.02710 (2019) - 2018
- [j37]J. Nathan Kutz, Joshua L. Proctor, Steven L. Brunton:
Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems. Complex. 2018: 6010634:1-6010634:16 (2018) - [j36]Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz:
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets. Frontiers Comput. Neurosci. 12: 102 (2018) - [j35]Rouzbeh Davoudi, Gregory R. Miller, J. Nathan Kutz:
Structural Load Estimation Using Machine Vision and Surface Crack Patterns for Shear-Critical RC Beams and Slabs. J. Comput. Civ. Eng. 32(4) (2018) - [j34]Eurika Kaiser, Marek Morzynski, Guillaume Daviller, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton:
Sparsity enabled cluster reduced-order models for control. J. Comput. Phys. 352: 388-409 (2018) - [j33]N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz:
Randomized nonnegative matrix factorization. Pattern Recognit. Lett. 104: 1-7 (2018) - [j32]Travis Askham, J. Nathan Kutz:
Variable Projection Methods for an Optimized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 17(1): 380-416 (2018) - [j31]Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Generalizing Koopman Theory to Allow for Inputs and Control. SIAM J. Appl. Dyn. Syst. 17(1): 909-930 (2018) - [j30]Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz:
Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm. SIAM J. Sci. Comput. 40(1) (2018) - [c8]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Discovering Conservation Laws from Data for Control. CDC 2018: 6415-6421 - [c7]Charles B. Delahunt, J. Nathan Kutz:
A moth brain learns to read MNIST. ICLR (Workshop) 2018 - [i23]Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz:
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets. CoRR abs/1802.02678 (2018) - [i22]Charles B. Delahunt, J. Nathan Kutz:
Putting a bug in ML: The moth olfactory network learns to read MNIST. CoRR abs/1802.05405 (2018) - [i21]N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton, J. Nathan Kutz:
Diffusion Maps meet Nyström. CoRR abs/1802.08762 (2018) - [i20]N. Benjamin Erichson, Peng Zeng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
Sparse Principal Component Analysis via Variable Projection. CoRR abs/1804.00341 (2018) - [i19]Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
Sparse Relaxed Regularized Regression: SR3. CoRR abs/1807.05411 (2018) - [i18]Charles B. Delahunt, J. Nathan Kutz:
Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data. CoRR abs/1808.08124 (2018) - [i17]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Discovering conservation laws from data for control. CoRR abs/1811.00961 (2018) - [i16]Krithika Manohar, J. Nathan Kutz, Steven L. Brunton:
Optimal Sensor and Actuator Placement using Balanced Model Reduction. CoRR abs/1812.01574 (2018) - 2017
- [j29]Megan Morrison, Pedro D. Maia, J. Nathan Kutz:
Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics. Comput. Math. Methods Medicine 2017: 6102494:1-6102494:13 (2017) - [j28]James M. Kunert-Graf, Eli Shlizerman, Andrew Walker, J. Nathan Kutz:
Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics. Frontiers Comput. Neurosci. 11: 53 (2017) - [j27]Pedro D. Maia, J. Nathan Kutz:
Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases. J. Comput. Neurosci. 42(3): 323-347 (2017) - [j26]James M. Kunert, Pedro D. Maia, J. Nathan Kutz:
Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage. PLoS Comput. Biol. 13(1) (2017) - [j25]James M. Kunert, Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion. PLoS Comput. Biol. 13(1) (2017) - [j24]Alessandro Alla, J. Nathan Kutz:
Nonlinear Model Order Reduction via Dynamic Mode Decomposition. SIAM J. Sci. Comput. 39(5) (2017) - [c6]Christoph A. Keller, Mathew J. Evans, J. Nathan Kutz, Steven Pawson:
Machine learning and air quality modeling. IEEE BigData 2017: 4570-4576 - [c5]J. Nathan Kutz, Samuel H. Rudy, Alessandro Alla, Steven L. Brunton:
Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology. CAMSAP 2017: 1-5 - [c4]Seth D. Pendergrass, Steven L. Brunton, J. Nathan Kutz, N. Benjamin Erichson, Travis Askham:
Dynamic Mode Decomposition for Background Modeling. ICCV Workshops 2017: 1862-1870 - [c3]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed Singular Value Decomposition for Image and Video Processing. ICCV Workshops 2017: 1880-1888 - [i15]Krithika Manohar, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton:
Data-Driven Sparse Sensor Placement. CoRR abs/1701.07569 (2017) - [i14]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Randomized Dynamic Mode Decomposition. CoRR abs/1702.02912 (2017) - [i13]N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz:
Randomized CP Tensor Decomposition. CoRR abs/1703.09074 (2017) - [i12]Zhe Bai, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, Steven L. Brunton:
Dynamic mode decomposition for compressive system identification. CoRR abs/1710.07737 (2017) - [i11]N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz:
Randomized Nonnegative Matrix Factorization. CoRR abs/1711.02037 (2017) - [i10]Thomas Baumeister, Steven L. Brunton, J. Nathan Kutz:
Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers. CoRR abs/1711.02702 (2017) - [i9]Bethany Lusch, J. Nathan Kutz, Steven L. Brunton:
Deep learning for universal linear embeddings of nonlinear dynamics. CoRR abs/1712.09707 (2017) - 2016
- [b1]J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor:
Dynamic mode decomposition - data-driven modeling of complex systems. SIAM 2016, ISBN 978-1-611-97449-2, pp. 1-234 - [j23]Ido Bright, Guang Lin, J. Nathan Kutz:
Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder. Multiscale Model. Simul. 14(2): 823-838 (2016) - [j22]Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Dynamic Mode Decomposition with Control. SIAM J. Appl. Dyn. Syst. 15(1): 142-161 (2016) - [j21]J. Nathan Kutz, Xing Fu, Steven L. Brunton:
Multiresolution Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 15(2): 713-735 (2016) - [j20]Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Sparse Sensor Placement Optimization for Classification. SIAM J. Appl. Math. 76(5): 2099-2122 (2016) - [j19]Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics. IEEE Trans. Mol. Biol. Multi Scale Commun. 2(1): 52-63 (2016) - [i8]Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz:
Online interpolation point refinement for reduced order models using a genetic algorithm. CoRR abs/1607.07702 (2016) - [i7]N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz:
Randomized Matrix Decompositions using R. CoRR abs/1608.02148 (2016) - [i6]Bethany Lusch, Eric C. Chi, J. Nathan Kutz:
Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries. CoRR abs/1608.04674 (2016) - [i5]Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton:
Streaming GPU Singular Value and Dynamic Mode Decompositions. CoRR abs/1612.07875 (2016) - 2015
- [c2]J. Nathan Kutz, Xing Fu, Steven L. Brunton, N. Benjamin Erichson:
Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking. ICCV Workshops 2015: 921-929 - [i4]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed Dynamic Mode Decomposition for Real-Time Object Detection. CoRR abs/1512.04205 (2015) - 2014
- [j18]Eli Shlizerman, Jeffrey Riffell, J. Nathan Kutz:
Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe. Frontiers Comput. Neurosci. 8: 70 (2014) - [j17]Pedro D. Maia, J. Nathan Kutz:
Identifying critical regions for spike propagation in axon segments. J. Comput. Neurosci. 36(2): 141-155 (2014) - [j16]Pedro D. Maia, J. Nathan Kutz:
Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury. J. Comput. Neurosci. 37(2): 317-332 (2014) - [j15]Varsha Dhankani, J. Nathan Kutz, Joshua T. Schiffer:
Herpes Simplex Virus-2 Genital Tract Shedding Is Not Predictable over Months or Years in Infected Persons. PLoS Comput. Biol. 10(11) (2014) - [j14]Benjamin Lansdell, Kevin Ford, J. Nathan Kutz:
A Reaction-Diffusion Model of Cholinergic Retinal Waves. PLoS Comput. Biol. 10(12) (2014) - [j13]Steven L. Brunton, Jonathan H. Tu, Ido Bright, J. Nathan Kutz:
Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems. SIAM J. Appl. Dyn. Syst. 13(4): 1716-1732 (2014) - [i3]Jacob Grosek, J. Nathan Kutz:
Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video. CoRR abs/1404.7592 (2014) - [i2]Jacob Grosek, J. Nathan Kutz:
Selecting a Small Set of Optimal Gestures from an Extensive Lexicon. CoRR abs/1404.7594 (2014) - 2013
- [j12]Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz:
Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition. Multiscale Model. Simul. 11(2): 522-544 (2013) - [j11]Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz:
Erratum: Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition. Multiscale Model. Simul. 11(4): 1311 (2013) - [c1]Feng Li, J. Nathan Kutz, Alexander Ping-Kong Wai:
Theoretical studies of frequency domain mode-locked fiber lasers. ICAIT 2013: 137-138 - [i1]Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Optimal Sensor Placement and Enhanced Sparsity for Classification. CoRR abs/1310.4217 (2013) - 2012
- [j10]Matthew O. Williams, Eli Shlizerman, Jon Wilkening, J. Nathan Kutz:
The Low Dimensionality of Time-Periodic Standing Waves in Water of Finite and Infinite Depth. SIAM J. Appl. Dyn. Syst. 11(3): 1033-1061 (2012) - [j9]Eli Shlizerman, Konrad Schroder, J. Nathan Kutz:
Neural Activity Measures and Their Dynamics. SIAM J. Appl. Math. 72(4): 1260-1291 (2012)
2000 – 2009
- 2007
- [j8]Joshua L. Proctor, J. Nathan Kutz:
Averaged models for passive mode-locking using nonlinear mode-coupling. Math. Comput. Simul. 74(4-5): 333-342 (2007) - [j7]Bernard Deconinck, Firat Kiyak, John D. Carter, J. Nathan Kutz:
SpectrUW: A laboratory for the numerical exploration of spectra of linear operators. Math. Comput. Simul. 74(4-5): 370-378 (2007) - 2006
- [j6]Bernard Deconinck, J. Nathan Kutz:
Computing spectra of linear operators using the Floquet-Fourier-Hill method. J. Comput. Phys. 219(1): 296-321 (2006) - [j5]J. Nathan Kutz:
Mode-Locked Soliton Lasers. SIAM Rev. 48(4): 629-678 (2006) - 2005
- [j4]Sarah E. Hewitt, J. Nathan Kutz:
Dynamics of the Optical Parametric Oscillator Near Resonance Detuning. SIAM J. Appl. Dyn. Syst. 4(4): 808-831 (2005) - 2002
- [j3]Bernard Deconinck, Bela A. Frigyik, J. Nathan Kutz:
Dynamics and Stability of Bose-Einstein Condensates: The Nonlinear Schrödinger Equation with Periodic Potential. J. Nonlinear Sci. 12(3): 169-205 (2002)
1990 – 1999
- 1999
- [j2]J. Nathan Kutz, Philip Holmes:
Dynamics and Bifurcations of a Planar Map Modeling Dispersion Managed Breathers. SIAM J. Appl. Math. 59(4): 1288-1302 (1999) - 1996
- [j1]J. Nathan Kutz, William L. Kath:
Stability of Pulses in Nonlinear Optical Fibers Using Phase-Sensitive Amplifiers. SIAM J. Appl. Math. 56(2): 611-626 (1996)
Coauthor Index
[j97] [j96] [j95] [j94] [j91] [c14] [i96] [i95] [i90] [i87] [i83] [j89] [j88] [c13] [c12] [i81] [i79] [i78] [i75] [i72] [i71] [i70] [j79] [j77] [j76] [j75] [j72] [j70] [j69] [i68] [i66] [i64] [i63] [i61] [j68] [j67] [j66] [j65] [j64] [c10] [i58] [i57] [i56] [i55] [i54] [i53] [i49] [i46] [i45] [i44] [j62] [j59] [j58] [j57] [j56] [j55] [j53] [i43] [i42] [i41] [i40] [i39] [i37] [i36] [i35] [i34] [i33] [j52] [j46] [j45] [j44] [j43] [j40] [j39] [j38] [i30] [i28] [i27] [i26] [i25] [i24] [j37] [j34] [j31] [j30] [c8] [i21] [i20] [i19] [i17] [i16] [j25] [c5] [c4] [c3] [i15] [i14] [i13] [i12] [i10] [i9] [b1] [j22] [j21] [j20] [j19] [i8] [i7] [i5] [c2] [i4] [j13] [i1]
Brian de Silva
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aka: Brian M. de Silva
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