166 related articles for article (PubMed ID: 35907719)
1. Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations.
Linot AJ; Graham MD
Chaos; 2022 Jul; 32(7):073110. PubMed ID: 35907719
[TBL] [Abstract][Full Text] [Related]
2. Deep learning delay coordinate dynamics for chaotic attractors from partial observable data.
Young CD; Graham MD
Phys Rev E; 2023 Mar; 107(3-1):034215. PubMed ID: 37073016
[TBL] [Abstract][Full Text] [Related]
3. The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory.
Oladyshkin S; Praditia T; Kroeker I; Mohammadi F; Nowak W; Otte S
Neural Netw; 2023 Sep; 166():85-104. PubMed ID: 37480771
[TBL] [Abstract][Full Text] [Related]
4. Analysis of chaotic saddles in high-dimensional dynamical systems: the Kuramoto-Sivashinsky equation.
Rempel EL; Chian AC; Macau EE; Rosa RR
Chaos; 2004 Sep; 14(3):545-56. PubMed ID: 15446964
[TBL] [Abstract][Full Text] [Related]
5. Learning a reduced basis of dynamical systems using an autoencoder.
Sondak D; Protopapas P
Phys Rev E; 2021 Sep; 104(3-1):034202. PubMed ID: 34654102
[TBL] [Abstract][Full Text] [Related]
6. An in-depth numerical study of the two-dimensional Kuramoto-Sivashinsky equation.
Kalogirou A; Keaveny EE; Papageorgiou DT
Proc Math Phys Eng Sci; 2015 Jul; 471(2179):20140932. PubMed ID: 26345218
[TBL] [Abstract][Full Text] [Related]
7. Deep learning to discover and predict dynamics on an inertial manifold.
Linot AJ; Graham MD
Phys Rev E; 2020 Jun; 101(6-1):062209. PubMed ID: 32688613
[TBL] [Abstract][Full Text] [Related]
8. Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics.
Vlachas PR; Pathak J; Hunt BR; Sapsis TP; Girvan M; Ott E; Koumoutsakos P
Neural Netw; 2020 Jun; 126():191-217. PubMed ID: 32248008
[TBL] [Abstract][Full Text] [Related]
9. Emergent Spaces for Coupled Oscillators.
Thiem TN; Kooshkbaghi M; Bertalan T; Laing CR; Kevrekidis IG
Front Comput Neurosci; 2020; 14():36. PubMed ID: 32528268
[TBL] [Abstract][Full Text] [Related]
10. Global and local reduced models for interacting, heterogeneous agents.
Thiem TN; Kemeth FP; Bertalan T; Laing CR; Kevrekidis IG
Chaos; 2021 Jul; 31(7):073139. PubMed ID: 34340348
[TBL] [Abstract][Full Text] [Related]
11. Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds.
Liu A; Axås J; Haller G
Chaos; 2024 Mar; 34(3):. PubMed ID: 38531092
[TBL] [Abstract][Full Text] [Related]
12. Nonlinear dynamics of a dispersive anisotropic Kuramoto-Sivashinsky equation in two space dimensions.
Tomlin RJ; Kalogirou A; Papageorgiou DT
Proc Math Phys Eng Sci; 2018 Mar; 474(2211):20170687. PubMed ID: 29662339
[TBL] [Abstract][Full Text] [Related]
13. Neural ordinary differential equations with irregular and noisy data.
Goyal P; Benner P
R Soc Open Sci; 2023 Jul; 10(7):221475. PubMed ID: 37476515
[TBL] [Abstract][Full Text] [Related]
14. Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning.
Matsuki T; Shibata K
Neural Netw; 2020 Dec; 132():19-29. PubMed ID: 32861145
[TBL] [Abstract][Full Text] [Related]
15. Projecting low-dimensional chaos from spatiotemporal dynamics in a model for plastic instability.
Sarmah R; Ananthakrishna G
Phys Rev E Stat Nonlin Soft Matter Phys; 2012 Nov; 86(5 Pt 2):056208. PubMed ID: 23214858
[TBL] [Abstract][Full Text] [Related]
16. Reconstruction, forecasting, and stability of chaotic dynamics from partial data.
Özalp E; Margazoglou G; Magri L
Chaos; 2023 Sep; 33(9):. PubMed ID: 37671991
[TBL] [Abstract][Full Text] [Related]
17. Chaotic interactions of self-replicating RNA.
Forst CV
Comput Chem; 1996 Mar; 20(1):69-83. PubMed ID: 16718865
[TBL] [Abstract][Full Text] [Related]
18. Accelerating Neural ODEs Using Model Order Reduction.
Lehtimaki M; Paunonen L; Linne ML
IEEE Trans Neural Netw Learn Syst; 2024 Jan; 35(1):519-531. PubMed ID: 35617183
[TBL] [Abstract][Full Text] [Related]
19. Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches.
Ricardo Constante-Amores C; Linot AJ; Graham MD
Chaos; 2024 Apr; 34(4):. PubMed ID: 38572942
[TBL] [Abstract][Full Text] [Related]
20. Estimating the Dimension of an Inertial Manifold from Unstable Periodic Orbits.
Ding X; Chaté H; Cvitanović P; Siminos E; Takeuchi KA
Phys Rev Lett; 2016 Jul; 117(2):024101. PubMed ID: 27447508
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]