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OverviewData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics. Full Product DetailsAuthor: Henry D. I. Abarbanel (University of California, San Diego)Publisher: Cambridge University Press Imprint: Cambridge University Press Edition: New edition Dimensions: Width: 17.30cm , Height: 1.40cm , Length: 25.00cm Weight: 0.520kg ISBN: 9781316519639ISBN 10: 1316519635 Pages: 204 Publication Date: 17 February 2022 Audience: Professional and scholarly , College/higher education , Professional & Vocational , Tertiary & Higher Education Format: Hardback Publisher's Status: Active Availability: In stock  We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of Contents1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.ReviewsAuthor InformationHenry D. I. Abarbanel has worked in several fields of physics including high energy physics, nonlinear dynamics, and data assimilation in neurobiology. He is the author of two previous books: Analysis of Observed Chaotic Data (1996) and Predicting the Future: Completing Models of Observed Complex Systems (2013). He is a Distinguished Professor of Physics at University of California, San Diego (UCSD) and a Distinguished Research Physicist at UCSD's Scripps Institution of Oceanography. Tab Content 6Author Website:Countries AvailableAll regions |
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