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Volume 9, 2022

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Call for Papers: Special Issue of JCD on “Computation of Lyapunov functions and contraction metrics” (click to view details)

Journal of Computational Dynamics (JCD) 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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Preface: Special issue on continuation methods and applications
Bernd Krauskopf and Hinke M. Osinga
2022, 9(3) : i-ii doi: 10.3934/jcd.2022015 +[Abstract](274) +[HTML](55) +[PDF](76.69KB)
Sensitivity analysis for periodic orbits and quasiperiodic invariant tori using the adjoint method
Harry Dankowicz and Jan Sieber
2022, 9(3) : 329-369 doi: 10.3934/jcd.2022006 +[Abstract](383) +[HTML](92) +[PDF](863.3KB)
Continuation methods for principal foliations of embedded surfaces
John Guckenheimer
2022, 9(3) : 371-392 doi: 10.3934/jcd.2022007 +[Abstract](340) +[HTML](111) +[PDF](1605.11KB)
Determining the global manifold structure of a continuous-time heterodimensional cycle
Andy Hammerlindl, Bernd Krauskopf, Gemma Mason and Hinke M. Osinga
2022, 9(3) : 393-419 doi: 10.3934/jcd.2022008 +[Abstract](376) +[HTML](120) +[PDF](2038.81KB)
Bistability, bifurcations and chaos in the Mackey-Glass equation
Valentin Duruisseaux and Antony R. Humphries
2022, 9(3) : 421-450 doi: 10.3934/jcd.2022009 +[Abstract](381) +[HTML](83) +[PDF](12640.84KB)
Computation of nonreciprocal dynamics in nonlinear materials
Behrooz Yousefzadeh
2022, 9(3) : 451-464 doi: 10.3934/jcd.2022010 +[Abstract](378) +[HTML](131) +[PDF](1413.28KB)
Emergence of quasiperiodic regimes in a neutral delay model of flute-like instruments: Influence of the detuning between resonance frequencies
Soizic Terrien, Christophe Vergez, Benoît Fabre and Patricio de la Cuadra
2022, 9(3) : 465-482 doi: 10.3934/jcd.2022011 +[Abstract](367) +[HTML](96) +[PDF](1267.81KB)
A novel sensing concept utilizing targeted, complex, nonlinear MEMS dynamics
Seigan Hayashi, Chris J. Cameron and Stefanie Gutschmidt
2022, 9(3) : 483-503 doi: 10.3934/jcd.2022012 +[Abstract](425) +[HTML](156) +[PDF](1783.99KB)
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](27071) +[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](8332) +[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](13038) +[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](9560) +[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](5118) +[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](5561) +[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](5325) +[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](4896) +[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](4670) +[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](6783) +[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](27071) +[PDF](1657.5KB) PDF Downloads(3493)
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](13038) +[PDF](9556.1KB) PDF Downloads(1576)
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](4287) +[HTML](668) +[PDF](6147.66KB) PDF Downloads(1265)
Uncertainty in finite-time Lyapunov exponent computations
Sanjeeva Balasuriya
2020, 7(2) : 313-337 doi: 10.3934/jcd.2020013 +[Abstract](3109) +[HTML](690) +[PDF](2929.21KB) PDF Downloads(1177)
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](9560) +[PDF](2004.7KB) PDF Downloads(879)
Computing Lyapunov functions using deep neural networks
Lars Grüne
2021, 8(2) : 131-152 doi: 10.3934/jcd.2021006 +[Abstract](2761) +[HTML](683) +[PDF](3923.59KB) PDF Downloads(805)
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](8332) +[PDF](1682.6KB) PDF Downloads(746)
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](2305) +[HTML](650) +[PDF](669.04KB) PDF Downloads(691)
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](6745) +[HTML](701) +[PDF](13381.36KB) PDF Downloads(669)
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](2733) +[HTML](509) +[PDF](672.31KB) PDF Downloads(504)

2021 CiteScore: 1.7

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