36 citations as recorded by crossref.

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2. Distinguishing the effects of internal and forced atmospheric variability in climate networks J. Deza et al. https://doi.org/10.5194/npg-21-617-2014
3. An Overview of Machine Learning and Deep Learning Applications in Earth Sciences in 2024: Achievements and Perspectives M. Krinitskiy https://doi.org/10.3103/S0027134924702217
4. Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity M. Mahecha et al. https://doi.org/10.1016/j.patrec.2010.06.021
5. How complex climate networks complement eigen techniques for the statistical analysis of climatological data J. Donges et al. https://doi.org/10.1007/s00382-015-2479-3
6. Nonlinear dimensionality reduction: Alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data M. Mahecha et al. https://doi.org/10.1016/j.ecoinf.2007.05.002
7. Data-driven stochastic model for cross-interacting processes with different time scales A. Gavrilov et al. https://doi.org/10.1063/5.0077302
8. Modelling multivariate data systems: application to El Niño and the Annual Cycle A. Gámez https://doi.org/10.1088/1367-2630/9/12/432
9. Spatiotemporal multiscaling analysis of impurity transport in plasma turbulence using proper orthogonal decomposition S. Futatani et al. https://doi.org/10.1063/1.3095865
10. Optimal model-free prediction from multivariate time series J. Runge et al. https://doi.org/10.1103/PhysRevE.91.052909
11. Low-frequency variability of European runoff L. Gudmundsson et al. https://doi.org/10.5194/hess-15-2853-2011
12. Principal nonlinear dynamical modes of climate variability D. Mukhin et al. https://doi.org/10.1038/srep15510
13. Nonlinear manifold learning for model reduction in finite elastodynamics D. Millán & M. Arroyo https://doi.org/10.1016/j.cma.2013.04.007
14. Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction P. Das et al. https://doi.org/10.1073/pnas.0603553103
15. Using Isomap to differentiate between anthropogenic and natural effects on groundwater dynamics in a complex geological setting S. Böttcher et al. https://doi.org/10.1016/j.jhydrol.2014.09.048
16. Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network A. Seleznev et al. https://doi.org/10.1063/1.5128372
17. Method for reconstructing nonlinear modes with adaptive structure from multidimensional data A. Gavrilov et al. https://doi.org/10.1063/1.4968852
18. Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptions T. Kittel et al. https://doi.org/10.1140/epjs/s11734-021-00269-9
19. Tracing biogeochemical processes in stream water and groundwater using non-linear statistics G. Lischeid & J. Bittersohl https://doi.org/10.1016/j.jhydrol.2008.03.013
20. Which processes prevail? C. Schilli et al. https://doi.org/10.1016/j.geoderma.2010.06.014
21. 20th century intraseasonal Asian monsoon dynamics viewed from Isomap A. Hannachi & A. Turner https://doi.org/10.5194/npg-20-725-2013
22. Bayesian optimization of empirical model with state-dependent stochastic forcing A. Gavrilov et al. https://doi.org/10.1016/j.chaos.2017.08.032
23. Revealing biogeographical patterns by nonlinear ordinations and derived anisotropic spatial filters M. Mahecha & S. Schmidtlein https://doi.org/10.1111/j.1466-8238.2007.00368.x
24. Probabilistic modeling of multivariate extreme non-Gaussian wind loads and its applications in envelope engineering B. Cui & P. Huang https://doi.org/10.1016/j.jobe.2024.111119
25. Simulation of Complex Systems Using the Observed Data Based on Recurrent Artificial Neural Networks A. Seleznev et al. https://doi.org/10.1007/s11141-019-09945-2
26. Subsurface Utility Extraction and Characterization: Combining GPR Symmetry and Polarization Attributes U. Boniger & J. Tronicke https://doi.org/10.1109/TGRS.2011.2163413
27. Learning a Weather Dictionary of Atmospheric Patterns Using Latent Dirichlet Allocation L. Fery et al. https://doi.org/10.1029/2021GL096184
28. Linear dynamical modes as new variables for data-driven ENSO forecast A. Gavrilov et al. https://doi.org/10.1007/s00382-018-4255-7
29. Nonlinear reconstruction of global climate leading modes on decadal scales D. Mukhin et al. https://doi.org/10.1007/s00382-017-4013-2
30. Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis J. Donges et al. https://doi.org/10.5194/npg-18-545-2011
31. An adaptive sampling algorithm for reduced‐order models using Isomap R. Halder et al. https://doi.org/10.1002/nme.7427
32. Coherent vorticity extraction in resistive drift-wave turbulence: Comparison of orthogonal wavelets versus proper orthogonal decomposition S. Futatani et al. https://doi.org/10.1016/j.crhy.2010.12.004
33. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. https://doi.org/10.1175/AIES-D-21-0011.1
34. Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment G. Lischeid https://doi.org/10.1007/s00477-008-0266-y
35. Analysis of 20th century surface air temperature using linear dynamical modes A. Gavrilov et al. https://doi.org/10.1063/5.0028246
36. Detecting dominant changes in irregularly sampled multivariate water quality data sets C. Lehr et al. https://doi.org/10.5194/hess-22-4401-2018