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.
Author(s): Henry D. I. Abarbanel
Publisher: Cambridge University Press
Year: 2022
Language: English
Pages: 350
City: Cambridge
00.0
01.0_pp_i_iv_Frontmatter
02.0_pp_v_viii_Contents
03.0_pp_ix_xviii_Preface
04.0_pp_1_4_A_Data_Assimilation_Reminder
05.0_pp_5_13_Remembrance_of_Things_Path
06.0_pp_14_25_SDA_Variational_Principles
07.0_pp_26_46_Using_Waveform_Information
08.0_pp_47_65_Annealing_in_the_Model_Precision_R_f
09.0_pp_66_94_Discrete_Time_Integration_in_Data_Assimilation_Variational_Principles_Lagrangian_and_Hamiltonian_For
10.0_pp_95_118_Monte_Carlo_Methods
11.0_pp_119_139_Machine_Learning_and_Its_Equivalence_to_Statistical_Data_Assimilation
12.0_pp_140_171_Two_Examples_of_the_Practical_Use_of_Data_Assimilation
13.0_pp_172_173_Unfinished_Business
14.0_pp_174_182_Bibliography
15.0_pp_183_188_Index
