The Statistical Physics of Data Assimilation and Machine Learning_Henry D. I. Abarbanel.pdf

THE STATISTICAL PHYSICS OF DATA ASSIMILATION AND MACHINE LEARNING 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 par- ticular emphasis on recent developments from machine learning and its relation to data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, is developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modeling 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. HENRY D.I.ABARBANEL has worked in several fields of physics including high energy physics, nonlinear dynamics, and data assimilation in neurobiology. He is the author of two previous books:Analysis of Observed Chaotic Data(1996) andPredicting the Future: Completing Models of Observed Complex Systems(2013). He is a Distin- guished Professor of Physics at the University of California, San Diego (UCSD) and a Distinguished Research Physicist at UCSD’s Scripps Institution of Oceanography. THE STATISTICAL PHYSICS OF DATA ASSIMILATION AND MACHINE LEARNING HENRY D. I. ABARBANEL University of California, San Diego University Printing House, Cambridge CB2 8BS, United Kingdom One Liberty Plaza, 20th Floor, New York, NY 10006, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia 314–321, 3rd Floor, Plot 3, Splendor Forum, Jasola District Centre, New Delhi – 110025, India 103 Penang Road, #05–06/07, Visioncrest Commercial, Singapore 238467 Cambridge University Press is