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  3. The Statistical Physics of Data Assimilation and Machine Learning
  • Cited by 5
Publisher:
Cambridge University Press
Online publication date:
January 2022
Print publication year:
2022
Online ISBN:
9781009024846
DOI:
https://doi.org/10.1017/9781009024846
Subjects:
Physics and Astronomy, Computer Science, Pattern Recognition and Machine Learning, Statistical Physics
Information

Book description

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.

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