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The statistical physics of data assimilation and machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The statistical physics of data assimilation and machine learning // Henry D. I. Abarbanel.
Author:	Abarbanel, H. D. I.,
Description:	1 online resource (xvii, 187 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 28 Jan 2022).
Subject:	Statistical physics - Data processing. -
Online resource:	https://doi.org/10.1017/9781009024846
ISBN:	9781009024846 (ebook)

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

The statistical physics of data assimilation and machine learning /Henry D. I. Abarbanel. - 1 online resource (xvii, 187 pages) :digital, PDF file(s).

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: 9781009024846 (ebook)Subjects--Topical Terms:

1405869
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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505 0 $a 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.
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.
650 0 $a Statistical physics $x Data processing. $3 1405869
650 0 $a Discrete-time systems. $3 556563
650 0 $a Supervised learning (Machine learning) $x Mathematical models. $3 1405870
650 0 $a Stochastic processes. $3 528256
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