139 related articles for article (PubMed ID: 37831789)
1. Optimizing the combination of data-driven and model-based elements in hybrid reservoir computing.
Duncan D; Räth C
Chaos; 2023 Oct; 33(10):. PubMed ID: 37831789
[TBL] [Abstract][Full Text] [Related]
2. Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.
Wikner A; Harvey J; Girvan M; Hunt BR; Pomerance A; Antonsen T; Ott E
Neural Netw; 2024 Feb; 170():94-110. PubMed ID: 37977092
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Data-informed reservoir computing for efficient time-series prediction.
Köster F; Patel D; Wikner A; Jaurigue L; Lüdge K
Chaos; 2023 Jul; 33(7):. PubMed ID: 37408150
[TBL] [Abstract][Full Text] [Related]
5. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model.
Pathak J; Wikner A; Fussell R; Chandra S; Hunt BR; Girvan M; Ott E
Chaos; 2018 Apr; 28(4):041101. PubMed ID: 31906641
[TBL] [Abstract][Full Text] [Related]
6. Short- and long-term predictions of chaotic flows and extreme events: a physics-constrained reservoir computing approach.
Doan NAK; Polifke W; Magri L
Proc Math Phys Eng Sci; 2021 Sep; 477(2253):20210135. PubMed ID: 35153579
[TBL] [Abstract][Full Text] [Related]
7. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.
Pathak J; Hunt B; Girvan M; Lu Z; Ott E
Phys Rev Lett; 2018 Jan; 120(2):024102. PubMed ID: 29376715
[TBL] [Abstract][Full Text] [Related]
8. Constructing polynomial libraries for reservoir computing in nonlinear dynamical system forecasting.
Ren HH; Bai YL; Fan MH; Ding L; Yue XX; Yu QH
Phys Rev E; 2024 Feb; 109(2-1):024227. PubMed ID: 38491629
[TBL] [Abstract][Full Text] [Related]
9. Predicting chaotic dynamics from incomplete input via reservoir computing with (D+1)-dimension input and output.
Shi L; Yan Y; Wang H; Wang S; Qu SX
Phys Rev E; 2023 May; 107(5-1):054209. PubMed ID: 37329034
[TBL] [Abstract][Full Text] [Related]
10. A hybrid proper orthogonal decomposition and next generation reservoir computing approach for high-dimensional chaotic prediction: Application to flow-induced vibration of tube bundles.
Liu T; Zhao X; Sun P; Zhou J
Chaos; 2024 Mar; 34(3):. PubMed ID: 38490185
[TBL] [Abstract][Full Text] [Related]
11. Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study.
Shahi S; Fenton FH; Cherry EM
Mach Learn Appl; 2022 Jun; 8():. PubMed ID: 35755176
[TBL] [Abstract][Full Text] [Related]
12. Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components.
Wikner A; Pathak J; Hunt BR; Szunyogh I; Girvan M; Ott E
Chaos; 2021 May; 31(5):053114. PubMed ID: 34240950
[TBL] [Abstract][Full Text] [Related]
13. Model-free forecasting of partially observable spatiotemporally chaotic systems.
Gupta V; Li LKB; Chen S; Wan M
Neural Netw; 2023 Mar; 160():297-305. PubMed ID: 36716509
[TBL] [Abstract][Full Text] [Related]
14. Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.
Chepuri R; Amzalag D; Antonsen TM; Girvan M
Chaos; 2024 Jun; 34(6):. PubMed ID: 38838103
[TBL] [Abstract][Full Text] [Related]
15. Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data.
Pathak J; Lu Z; Hunt BR; Girvan M; Ott E
Chaos; 2017 Dec; 27(12):121102. PubMed ID: 29289043
[TBL] [Abstract][Full Text] [Related]
16. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.
Vlachas PR; Byeon W; Wan ZY; Sapsis TP; Koumoutsakos P
Proc Math Phys Eng Sci; 2018 May; 474(2213):20170844. PubMed ID: 29887750
[TBL] [Abstract][Full Text] [Related]
17. Existence of reservoir with finite-dimensional output for universal reservoir computing.
Sugiura S; Ariizumi R; Asai T; Azuma SI
Sci Rep; 2024 Apr; 14(1):8448. PubMed ID: 38600157
[TBL] [Abstract][Full Text] [Related]
18. Efficient forecasting of chaotic systems with block-diagonal and binary reservoir computing.
Ma H; Prosperino D; Haluszczynski A; Räth C
Chaos; 2023 Jun; 33(6):. PubMed ID: 37307160
[TBL] [Abstract][Full Text] [Related]
19. A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics.
Platt JA; Penny SG; Smith TA; Chen TC; Abarbanel HDI
Neural Netw; 2022 Sep; 153():530-552. PubMed ID: 35839598
[TBL] [Abstract][Full Text] [Related]
20. On prediction of chaotic dynamics in semiconductor lasers by reservoir computing.
Li XZ; Yang B; Zhao S; Gu Y; Zhao M
Opt Express; 2023 Nov; 31(24):40592-40603. PubMed ID: 38041355
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
