88 citations as recorded by crossref.

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10. Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing K. Yamashita & T. Hamagami 10.20965/jaciii.2022.p0562
11. 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
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28. 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
29. 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
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31. 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
32. Recurrent neural networks for partially observed dynamical systems U. Bhat & S. Munch 10.1103/PhysRevE.105.044205
33. Breaking symmetries of the reservoir equations in echo state networks J. Herteux & C. Räth 10.1063/5.0028993
34. 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
35. Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics A. Racca & L. Magri 10.1016/j.neunet.2021.05.004
36. 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
37. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
38. 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
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42. A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing C. Liu et al. 10.1080/00207543.2022.2056860
43. Data-driven prediction and control of extreme events in a chaotic flow A. Racca & L. Magri 10.1103/PhysRevFluids.7.104402
44. CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting C. Chen et al. 10.1063/5.0154777
45. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. 10.1029/2021MS002537
46. 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
47. 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
48. 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
49. Transformers for modeling physical systems N. Geneva & N. Zabaras 10.1016/j.neunet.2021.11.022
50. Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond I. Maksymov 10.3390/en16145366
51. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
52. Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing S. Shahi et al. 10.3389/fphys.2021.734178
53. Mechanics and thermodynamics of a new minimal model of the atmosphere G. Vissio & V. Lucarini 10.1140/epjp/s13360-020-00814-w
54. Predicting turbulent dynamics with the convolutional autoencoder echo state network A. Racca et al. 10.1017/jfm.2023.716
55. Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement S. Gumbarević et al. 10.3390/en15145029
56. 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
57. Direct data-driven forecast of local turbulent heat flux in Rayleigh–Bénard convection S. Pandey et al. 10.1063/5.0087977
58. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
59. Controlling nonlinear dynamical systems into arbitrary states using machine learning A. Haluszczynski & C. Räth 10.1038/s41598-021-92244-6
60. Predicting rare events using neural networks and short-trajectory data J. Strahan et al. 10.1016/j.jcp.2023.112152
61. «  Science des données  » versus science physique : la technologie des données nous conduit-elle vers une nouvelle synthèse ? V. Balaji 10.5802/crgeos.24
62. Symmetry-aware reservoir computing W. Barbosa et al. 10.1103/PhysRevE.104.045307
63. On the universal transformation of data-driven models to control systems S. Peitz & K. Bieker 10.1016/j.automatica.2022.110840
64. Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A. Chattopadhyay et al. 10.1017/eds.2022.30
65. 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
66. Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems S. Mondal et al. 10.1016/j.egyai.2021.100085
67. Analysis of a bistable climate toy model with physics-based machine learning methods M. Gelbrecht et al. 10.1140/epjs/s11734-021-00175-0
68. 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
69. Model-free control of dynamical systems with deep reservoir computing D. Canaday et al. 10.1088/2632-072X/ac24f3
70. On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction A. González-Zapata et al. 10.3390/electronics11213612
71. 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
72. Learning spatiotemporal chaos using next-generation reservoir computing W. Barbosa & D. Gauthier 10.1063/5.0098707
73. Predicting shallow water dynamics using echo-state networks with transfer learning X. Chen et al. 10.1007/s13137-022-00210-9
74. “Data science” versus physical science: is data technology leading us towards a new synthesis? R. Dziegielinski 10.5802/crgeos.24-en
75. 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
76. Reservoir Computing as a Tool for Climate Predictability Studies B. Nadiga 10.1029/2020MS002290
77. Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation S. Penny et al. 10.1029/2021MS002843
78. Symmetry kills the square in a multifunctional reservoir computer A. Flynn et al. 10.1063/5.0055699
79. Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems M. Yin et al. 10.1016/j.cma.2022.115027
80. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
81. A framework for machine learning of model error in dynamical systems M. Levine & A. Stuart 10.1090/cams/10
82. Data Assimilation with Missing Data in Nonstationary Environments for Probabilistic Machine Learning Models Y. Wei et al. 10.1016/j.jocs.2023.102151
83. Model-assisted deep learning of rare extreme events from partial observations A. Asch et al. 10.1063/5.0077646
84. LEARNING FINE SCALE DYNAMICS FROM COARSE OBSERVATIONS VIA INNER RECURRENCE V. Churchill & D. Xiu 10.1615/JMachLearnModelComput.2022044586
85. 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
86. Enhanced FPGA implementation of Echo State Networks for chaotic time series prediction A. Gonzalez-Zapata et al. 10.1016/j.vlsi.2023.05.002
87. Model-free forecasting of partially observable spatiotemporally chaotic systems V. Gupta et al. 10.1016/j.neunet.2023.01.013
88. Variational principles for fluid dynamics on rough paths D. Crisan et al. 10.1016/j.aim.2022.108409