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The statistical physics of data assi...

Abarbanel, Henry D. I.

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The statistical physics of data assimilation and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The statistical physics of data assimilation and machine learning/ Henry D. I. Abarbanel.
作者:	Abarbanel, Henry D. I.
出版者:	Cambridge ; New York, NY :Cambridge University Press, : 2022.,
面頁冊數:	xvii, 187 p. :ill., digital ;25 cm.
附註:	Title from publisher's bibliographic system (viewed on 28 Jan 2022).
內容註:	Prologue: Linking "the future" with the present -- A data assimilation reminder -- Remembrance of things path -- SDA variational principles; Euler-Lagrange equations and Hamiltonian formulation -- Using waveform information -- Annealing in the model precision Rf -- Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations -- Monte Carlo methods -- Machine learning and its equivalence to statistical data assimilation -- Two examples of the practical use of data assimilation -- Unfinished business.
標題:	Statistical physics - Data processing. -
電子資源:	https://doi.org/10.1017/9781009024846
ISBN:	9781009024846

The statistical physics of data assimilation and machine learning
Abarbanel, Henry D. I.

The statistical physics of data assimilation and machine learning[electronic resource] /Henry D. I. Abarbanel. - Cambridge ; New York, NY :Cambridge University Press,2022. - xvii, 187 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 28 Jan 2022).

Prologue: Linking "the future" with the present -- A data assimilation reminder -- Remembrance of things path -- SDA variational principles; Euler-Lagrange equations and Hamiltonian formulation -- Using waveform information -- Annealing in the model precision Rf -- Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations -- Monte Carlo methods -- Machine learning and its equivalence to statistical data assimilation -- Two examples of the practical use of data assimilation -- Unfinished business.

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.

ISBN: 9781009024846Subjects--Topical Terms:

731128
Statistical physics
--Data processing.

LC Class. No.: QC174.8 / .A225 2022

Dewey Class. No.: 530.13

The statistical physics of data assimilation and machine learning
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500 $a Title from publisher's bibliographic system (viewed on 28 Jan 2022).
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520 $a 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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