The Statistical Physics of Data Assimilation and Machine Learning

Henry D. I. Abarbanel (University of California, San Diego)

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English

Cambridge University Press
17 February 2022

Statistical physics; Data capture & analysis; Machine learning

Data 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.

By: Henry D. I. Abarbanel (University of California San Diego)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Edition: New edition
Dimensions: Height: 250mm, Width: 173mm, Spine: 14mm
Weight: 520g
ISBN: 9781316519639
ISBN 10: 1316519635
Pages: 204
Publication Date: 17 February 2022
Audience: Professional and scholarly , College/higher education , Undergraduate , Primary
Format: Hardback
Publisher's Status: Active

1. 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.

Henry 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.