ISSN:
 2158-2491

eISSN:
 2158-2505

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Volume 8, 2021

Volume 7, 2020

Volume 6, 2019

Volume 3, 2016

Volume 2, 2015

Volume 1, 2014





JCD Flyer: showing all essential information of the journal.
Call for Papers: Special Issue of JCD on “Computation of Lyapunov functions and contraction metrics” (click to view details)

Journal of Computational Dynamics (JCD), platinum OA (i.e., Open Access and no publication fees), is focused on the intersection of computation with deterministic and stochastic dynamics. The mission of the journal is to publish papers that explore new computational methods for analyzing dynamic problems or use novel dynamical methods to improve computation. The subject matter of JCD includes both fundamental mathematical contributions and applications to problems from science and engineering. A non-exhaustive list of topics includes

  *  Computation of phase-space structures and bifurcations
  *  Multi-time-scale methods
  *  Structure-preserving integration
  *  Nonlinear and stochastic model reduction
  *  Set-valued numerical techniques
  *  Network and distributed dynamics


Note: “Most Cited” is by Cross-Ref , and “Most Downloaded” is based on available data in the new website.

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Computing Reeb dynamics on four-dimensional convex polytopes
Julian Chaidez and Michael Hutchings
2021, 8(4) : 403-445 doi: 10.3934/jcd.2021016 +[Abstract](351) +[HTML](193) +[PDF](572.97KB)
Tracking the critical points of curves evolving under planar curvature flows
Eszter Fehér, Gábor Domokos and Bernd Krauskopf
2021, 8(4) : 447-494 doi: 10.3934/jcd.2021017 +[Abstract](211) +[HTML](97) +[PDF](5208.39KB)
Classification with Runge-Kutta networks and feature space augmentation
Elisa Giesecke and Axel Kröner
2021, 8(4) : 495-520 doi: 10.3934/jcd.2021018 +[Abstract](197) +[HTML](63) +[PDF](5286.25KB)
On dynamic mode decomposition: Theory and applications
Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton and J. Nathan Kutz
2014, 1(2) : 391-421 doi: 10.3934/jcd.2014.1.391 +[Abstract](22341) +[PDF](1657.5KB) Cited By(341)
A kernel-based method for data-driven koopman spectral analysis
Matthew O. Williams, Clarence W. Rowley and Ioannis G. Kevrekidis
2015, 2(2) : 247-265 doi: 10.3934/jcd.2015005 +[Abstract](7184) +[PDF](1682.6KB) Cited By(64)
Compressed sensing and dynamic mode decomposition
Steven L. Brunton, Joshua L. Proctor, Jonathan H. Tu and J. Nathan Kutz
2015, 2(2) : 165-191 doi: 10.3934/jcd.2015002 +[Abstract](10637) +[PDF](9556.1KB) Cited By(55)
On the numerical approximation of the Perron-Frobenius and Koopman operator
Stefan Klus, Péter Koltai and Christof Schütte
2016, 3(1) : 51-79 doi: 10.3934/jcd.2016003 +[Abstract](8058) +[PDF](2004.7KB) Cited By(20)
Detecting isolated spectrum of transfer and Koopman operators with Fourier analytic tools
Gary Froyland, Cecilia González-Tokman and Anthony Quas
2014, 1(2) : 249-278 doi: 10.3934/jcd.2014.1.249 +[Abstract](4373) +[PDF](8600.4KB) Cited By(12)
Modularity revisited: A novel dynamics-based concept for decomposing complex networks
Marco Sarich, Natasa Djurdjevac Conrad, Sharon Bruckner, Tim O. F. Conrad and Christof Schütte
2014, 1(1) : 191-212 doi: 10.3934/jcd.2014.1.191 +[Abstract](4816) +[PDF](1083.3KB) Cited By(12)
Optimal control of multiscale systems using reduced-order models
Carsten Hartmann, Juan C. Latorre, Wei Zhang and Grigorios A. Pavliotis
2014, 1(2) : 279-306 doi: 10.3934/jcd.2014.1.279 +[Abstract](4583) +[PDF](1246.3KB) Cited By(11)
Attraction-based computation of hyperbolic Lagrangian coherent structures
Daniel Karrasch, Mohammad Farazmand and George Haller
2015, 2(1) : 83-93 doi: 10.3934/jcd.2015.2.83 +[Abstract](4159) +[PDF](2835.1KB) Cited By(10)
Steady state bifurcations for the Kuramoto-Sivashinsky equation: A computer assisted proof
Piotr Zgliczyński
2015, 2(1) : 95-142 doi: 10.3934/jcd.2015.2.95 +[Abstract](4081) +[PDF](623.1KB) Cited By(9)
Global invariant manifolds near a Shilnikov homoclinic bifurcation
Pablo Aguirre, Bernd Krauskopf and Hinke M. Osinga
2014, 1(1) : 1-38 doi: 10.3934/jcd.2014.1.1 +[Abstract](5946) +[PDF](6559.1KB) Cited By(9)
On dynamic mode decomposition: Theory and applications
Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton and J. Nathan Kutz
2014, 1(2) : 391-421 doi: 10.3934/jcd.2014.1.391 +[Abstract](22341) +[PDF](1657.5KB) PDF Downloads(3022)
Compressed sensing and dynamic mode decomposition
Steven L. Brunton, Joshua L. Proctor, Jonathan H. Tu and J. Nathan Kutz
2015, 2(2) : 165-191 doi: 10.3934/jcd.2015002 +[Abstract](10637) +[PDF](9556.1KB) PDF Downloads(1207)
Evaluating the accuracy of the dynamic mode decomposition
Hao Zhang, Scott T. M. Dawson, Clarence W. Rowley, Eric A. Deem and Louis N. Cattafesta
2020, 7(1) : 35-56 doi: 10.3934/jcd.2020002 +[Abstract](3241) +[HTML](629) +[PDF](6147.66KB) PDF Downloads(935)
On the numerical approximation of the Perron-Frobenius and Koopman operator
Stefan Klus, Péter Koltai and Christof Schütte
2016, 3(1) : 51-79 doi: 10.3934/jcd.2016003 +[Abstract](8058) +[PDF](2004.7KB) PDF Downloads(700)
A kernel-based method for data-driven koopman spectral analysis
Matthew O. Williams, Clarence W. Rowley and Ioannis G. Kevrekidis
2015, 2(2) : 247-265 doi: 10.3934/jcd.2015005 +[Abstract](7184) +[PDF](1682.6KB) PDF Downloads(625)
Uncertainty in finite-time Lyapunov exponent computations
Sanjeeva Balasuriya
2020, 7(2) : 313-337 doi: 10.3934/jcd.2020013 +[Abstract](2085) +[HTML](623) +[PDF](2929.21KB) PDF Downloads(617)
Deep learning as optimal control problems: Models and numerical methods
Martin Benning, Elena Celledoni, Matthias J. Ehrhardt, Brynjulf Owren and Carola-Bibiane Schönlieb
2019, 6(2) : 171-198 doi: 10.3934/jcd.2019009 +[Abstract](5369) +[HTML](649) +[PDF](13381.36KB) PDF Downloads(568)
Time-resolved denoising using model order reduction, dynamic mode decomposition, and kalman filter and smoother
Mojtaba F. Fathi, Ahmadreza Baghaie, Ali Bakhshinejad, Raphael H. Sacho and Roshan M. D'Souza
2020, 7(2) : 469-487 doi: 10.3934/jcd.2020019 +[Abstract](1744) +[HTML](579) +[PDF](669.04KB) PDF Downloads(518)
Computing Lyapunov functions using deep neural networks
Lars Grüne
2021, 8(2) : 131-152 doi: 10.3934/jcd.2021006 +[Abstract](1643) +[HTML](473) +[PDF](3923.59KB) PDF Downloads(491)
Approximation of Lyapunov functions from noisy data
Peter Giesl, Boumediene Hamzi, Martin Rasmussen and Kevin Webster
2020, 7(1) : 57-81 doi: 10.3934/jcd.2020003 +[Abstract](2019) +[HTML](479) +[PDF](672.31KB) PDF Downloads(415)

2020 CiteScore: 1

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