102 citations as recorded by crossref.

1. Prediction of spatiotemporal dynamic systems by data-driven reconstruction H. Ren et al. 10.1016/j.chaos.2024.115137
2. How to Calibrate a Dynamical System With Neural Network Based Physics? B. Balogh et al. 10.1029/2022GL097872
3. Data-driven forecasting of nonequilibrium solid-state dynamics S. Meinecke et al. 10.1103/PhysRevB.107.184306
4. Learning ocean circulation models with reservoir computing K. Yao et al. 10.1063/5.0119061
5. Predicting critical transitions in multiscale dynamical systems using reservoir computing S. Lim et al. 10.1063/5.0023764
6. Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods Z. Zhang et al. 10.5194/cp-18-2643-2022
7. Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics H. Arbabi & T. Sapsis 10.1137/20M1359833
8. Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing K. Yamashita & T. Hamagami 10.20965/jaciii.2022.p0562
9. Training a neural network to predict dynamics it has never seen A. Pershin et al. 10.1103/PhysRevE.107.014304
10. Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks R. Maddu et al. 10.2166/wcc.2021.460
11. Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity D. Patel et al. 10.1063/5.0042598
12. Neural echo state network using oscillations of gas bubbles in water I. Maksymov et al. 10.1103/PhysRevE.105.044206
13. Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning D. Köglmayr & C. Räth 10.1038/s41598-023-50726-9
14. Accuracy of neural networks for the simulation of chaotic dynamics: Precision of training data vs precision of the algorithm S. Bompas et al. 10.1063/5.0021264
15. Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning A. Subel et al. 10.1063/5.0040286
16. Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning Y. Guan et al. 10.1016/j.jcp.2022.111090
17. Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model S. Swagatika et al. 10.2166/wcc.2023.487
18. Recurrent neural networks for partially observed dynamical systems U. Bhat & S. Munch 10.1103/PhysRevE.105.044205
19. Mutually Adaptable Learning Q. Tan et al. 10.1109/TETCI.2023.3300183
20. Probabilistic optimal interpolation for data assimilation between machine learning model predictions and real time observations Y. Wei et al. 10.1016/j.jocs.2023.101977
21. A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings M. Khodkar & P. Hassanzadeh 10.1016/j.jcp.2021.110412
22. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
23. An automatic identification method of imbalanced lithology based on Deep Forest and K-means SMOTE X. Zhu et al. 10.1016/j.geoen.2023.211595
24. Optimizing the combination of data-driven and model-based elements in hybrid reservoir computing D. Duncan & C. Räth 10.1063/5.0164013
25. Equivalence of machine learning models in modeling chaos X. Chen et al. 10.1016/j.chaos.2022.112831
26. A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing C. Liu et al. 10.1080/00207543.2022.2056860
27. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. 10.1029/2021MS002537
28. Data-driven learning of chaotic dynamical systems using Discrete-Temporal Sobolev Networks C. Kennedy et al. 10.1016/j.neunet.2024.106152
29. Using machine learning to anticipate tipping points and extrapolate to post-tipping dynamics of non-stationary dynamical systems D. Patel & E. Ott 10.1063/5.0131787
30. Heterogeneity of the attractor of the Lorenz ’96 model: Lyapunov analysis, unstable periodic orbits, and shadowing properties C. Maiocchi et al. 10.1016/j.physd.2023.133970
31. Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model R. Parthipan et al. 10.5194/gmd-16-4501-2023
32. Transformers for modeling physical systems N. Geneva & N. Zabaras 10.1016/j.neunet.2021.11.022
33. Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing S. Shahi et al. 10.3389/fphys.2021.734178
34. Mechanics and thermodynamics of a new minimal model of the atmosphere G. Vissio & V. Lucarini 10.1140/epjp/s13360-020-00814-w
35. Deep learning-based state prediction of the Lorenz system with control parameters X. Wang et al. 10.1063/5.0187866
36. Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement S. Gumbarević et al. 10.3390/en15145029
37. Echo state network model for analyzing solar-wind effects on the AU and AL indices S. Nakano & R. Kataoka 10.5194/angeo-40-11-2022
38. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
39. Controlling nonlinear dynamical systems into arbitrary states using machine learning A. Haluszczynski & C. Räth 10.1038/s41598-021-92244-6
40. Reservoir computing for a MEMS mirror-based laser beam control on FPGA Y. Wang et al. 10.1007/s10043-024-00871-x
41. Symmetry-aware reservoir computing W. Barbosa et al. 10.1103/PhysRevE.104.045307
42. Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case R. Mojgani et al. 10.1063/5.0091282
43. Analysis of a bistable climate toy model with physics-based machine learning methods M. Gelbrecht et al. 10.1140/epjs/s11734-021-00175-0
44. Model-free control of dynamical systems with deep reservoir computing D. Canaday et al. 10.1088/2632-072X/ac24f3
45. Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin W. Zheng et al. 10.1016/j.marpetgeo.2020.104720
46. Time Series Prediction of ESN Based on Chebyshev Mapping and Strongly Connected Topology M. Xie et al. 10.1007/s11063-024-11474-7
47. “Data science” versus physical science: is data technology leading us towards a new synthesis? R. Dziegielinski 10.5802/crgeos.24-en
48. Symmetry kills the square in a multifunctional reservoir computer A. Flynn et al. 10.1063/5.0055699
49. Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems M. Yin et al. 10.1016/j.cma.2022.115027
50. Data Assimilation with Missing Data in Nonstationary Environments for Probabilistic Machine Learning Models Y. Wei et al. 10.1016/j.jocs.2023.102151
51. Model-assisted deep learning of rare extreme events from partial observations A. Asch et al. 10.1063/5.0077646
52. Enhanced FPGA implementation of Echo State Networks for chaotic time series prediction A. Gonzalez-Zapata et al. 10.1016/j.vlsi.2023.05.002
53. Model-free forecasting of partially observable spatiotemporally chaotic systems V. Gupta et al. 10.1016/j.neunet.2023.01.013
54. Learn to synchronize, synchronize to learn P. Verzelli et al. 10.1063/5.0056425
55. Variability of echo state network prediction horizon for partially observed dynamical systems A. Mahata et al. 10.1103/PhysRevE.108.064209
56. Data-driven and echo state network-based prediction of wave propagation behavior in dam-break flood C. Li et al. 10.2166/hydro.2023.035
57. Reconstructing coupled time series in climate systems using three kinds of machine-learning methods Y. Huang et al. 10.5194/esd-11-835-2020
58. Machine learning approach reveals strong link between obliquity amplitude increase and the Mid-Brunhes transition T. Mitsui & N. Boers 10.1016/j.quascirev.2021.107344
59. Next-generation reservoir computing based on memristor array K. Ren et al. 10.7498/aps.71.20220082
60. A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization N. Chen & Y. Zhang 10.1016/j.physd.2023.133743
61. Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science V. Balaji 10.1098/rsta.2020.0085
62. Reducing echo state network size with controllability matrices B. Whiteaker & P. Gerstoft 10.1063/5.0071926
63. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems A. Rupe et al. 10.1088/1367-2630/ac95b7
64. Prediction of Pan-Arctic Sea Ice Using Attention-Based LSTM Neural Networks J. Wei et al. 10.3389/fmars.2022.860403
65. Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series A. González-Zapata et al. 10.3390/math10203886
66. Computational Efficiency of Multi-Step Learning Echo State Networks for Nonlinear Time Series Prediction T. Akiyama & G. Tanaka 10.1109/ACCESS.2022.3158755
67. Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka M. Herath et al. 10.3390/s23073680
68. A sensitivity analysis of a regression model of ocean temperature R. Furner et al. 10.1017/eds.2022.10
69. DEEP LEARNING OF CHAOTIC SYSTEMS FROM PARTIALLY-OBSERVED DATA V. Churchill & D. Xiu 10.1615/JMachLearnModelComput.2022045602
70. BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations N. Chen & Y. Li 10.1063/5.0062028
71. Waveformer for modeling dynamical systems N. N. & S. Chakraborty 10.1016/j.ymssp.2024.111253
72. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada 10.5194/gmd-14-5623-2021
73. Breaking symmetries of the reservoir equations in echo state networks J. Herteux & C. Räth 10.1063/5.0028993
74. Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics A. Racca & L. Magri 10.1016/j.neunet.2021.05.004
75. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. 10.1029/2020GL092133
76. Data-driven prediction and control of extreme events in a chaotic flow A. Racca & L. Magri 10.1103/PhysRevFluids.7.104402
77. CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting C. Chen et al. 10.1063/5.0154777
78. Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond I. Maksymov 10.3390/en16145366
79. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
80. Interpretable predictions of chaotic dynamical systems using dynamical system deep learning M. Wang & J. Li 10.1038/s41598-024-53169-y
81. Predicting turbulent dynamics with the convolutional autoencoder echo state network A. Racca et al. 10.1017/jfm.2023.716
82. Direct data-driven forecast of local turbulent heat flux in Rayleigh–Bénard convection S. Pandey et al. 10.1063/5.0087977
83. Predicting rare events using neural networks and short-trajectory data J. Strahan et al. 10.1016/j.jcp.2023.112152
84. Investigating forced transient chaos in monsoon using Echo State Networks C. Kapil et al. 10.1007/s00382-024-07174-6
85. «  Science des données  » versus science physique : la technologie des données nous conduit-elle vers une nouvelle synthèse ? V. Balaji 10.5802/crgeos.24
86. On the universal transformation of data-driven models to control systems S. Peitz & K. Bieker 10.1016/j.automatica.2022.110840
87. Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A. Chattopadhyay et al. 10.1017/eds.2022.30
88. Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems S. Mondal et al. 10.1016/j.egyai.2021.100085
89. Constructing polynomial libraries for reservoir computing in nonlinear dynamical system forecasting H. Ren et al. 10.1103/PhysRevE.109.024227
90. Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study S. Shahi et al. 10.1016/j.mlwa.2022.100300
91. On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction A. González-Zapata et al. 10.3390/electronics11213612
92. Learning spatiotemporal chaos using next-generation reservoir computing W. Barbosa & D. Gauthier 10.1063/5.0098707
93. Predicting shallow water dynamics using echo-state networks with transfer learning X. Chen et al. 10.1007/s13137-022-00210-9
94. Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5 A. Chattopadhyay et al. 10.5194/gmd-15-2221-2022
95. Reservoir Computing as a Tool for Climate Predictability Studies B. Nadiga 10.1029/2020MS002290
96. Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation S. Penny et al. 10.1029/2021MS002843
97. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
98. A framework for machine learning of model error in dynamical systems M. Levine & A. Stuart 10.1090/cams/10
99. LEARNING FINE SCALE DYNAMICS FROM COARSE OBSERVATIONS VIA INNER RECURRENCE V. Churchill & D. Xiu 10.1615/JMachLearnModelComput.2022044586
100. Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A. Wikner et al. 10.1063/5.0048050
101. Variational principles for fluid dynamics on rough paths D. Crisan et al. 10.1016/j.aim.2022.108409
102. Nonintrusive Operator Inference for Chaotic Systems J. Almeida et al. 10.1109/TAI.2022.3207449