Filtering Complex Turbulent Systems

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Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks (2018, methodology): “tochastic Model (MSM) and Hybrid LSTM-MSM The MSM is a powerful data-driven method used to quantify uncertainty and perform forecasts in turbulent systems with high intrinsic attractor dimensionality [54, 36]. It is parameterized a priori to capture global statistical information of the attractor by”
Data Assimilation: A Mathematical Introduction (2015, background): “The development of new filters is a very active area of research and the reader is directed to the book [82], together with the articles [25],[83] and [114] for insight into some of the recent developments with an applied mathematics perspective.”
Challenges in Climate Science and Contemporary Applied Mathematics (2012, background): “The recent review paper [96] and graduate text [103] contain much more detailed material for the interested reader on this emerging viewpoint for FDA for turbulent dynamical systems.”