The Statistical Physics of Data Assimilation and Machine Learning
Published on 2. February 2022
978-1-009-02170-8 (ISBN)
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
College/higher education
Illustrations
Worked examples or Exercises
File size
12,08 MB
ISBN-13
978-1-009-02170-8 (9781009021708)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Henry D. I. Abarbanel
The Statistical Physics of Data Assimilation and Machine Learning
Book
02/2022
Cambridge University Press
€94.90
Shipment within 15-20 days
Person
Content
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.
