Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

By H. Ye, R. Beamish, S. Glaser, Sue C. H. Grant, C. Hsieh, L. Richards, J. Schnute, George Sugihara. 2015

358 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Nature and Landscape Conservation, Public Health, Environmental and Occupational Health

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Citations in the corpus

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (2018, background): “, 1999), empirical dynamic modeling (Sugihara et al., 2012; Ye et al., 2015), modeling emergent behavior (Roberts, 2014), automated inference of dynamics (Schmidt et al.”
Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, methodology): “These include methods to discover governing equations from time-series data (6), equation-free modeling (7), empirical dynamic modeling (8, 9), modeling emergent behavior (10), and automated inference of dynamics (11–13); ref.”
Hidden physics models: Machine learning of nonlinear partial differential equations (2017, background): “The literature on data-driven discovery of dynamical systems [3] is vast and encompasses equation-free modeling [4], artificial neural networks [5], nonlinear regression [6], empirical dynamic modeling [7,8], modeling emergent behavior [9], automated inference of dynamics [10–12], normal form identi”
Chaos as an intermittently forced linear system (2016, methodology): “In fact, forcing the system in (6) with other forcing signatures from the same statistics, for example by randomly shuffling the forcing time series, does not result in the same dynamics.”
Empirical dynamic modeling for beginners (2017, methodology): “To study dynamical systems, nonlinear time series analytical methods have been developed over recent decades (e.g. Sugihara and May 1990; Anderson et al. 2008; Glaser et al. 2014; Ye et al. 2015a).”
Data Driven Governing Equations Approximation Using Deep Neural Networks (2018, methodology): “…regression ([5, 43]), equation-free modeling [24], heterogeneous multi-scale method (HMM) ([15]), artiﬁcial neural networks ([19]), nonlinear regression ([50]), empirical dynamic modeling ([46, 53]), nonlinear Laplacian spectral analysis ([18]), automated inference of dynamics ([44, 12, 13]), etc.”
Data-driven discovery of coordinates and governing equations (2019, background): “Numerous recent approaches leverage machine learning to identify dynamical systems models from data [19–26], with many using neural networks to model time-series data [18, 27–38].”