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 particular emphasis on recent develop...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York, NY
Cambridge University Press
2022
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| Schlagworte: | |
| Online-Zugang: | DE-12 DE-92 DE-91 Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 28 Jan 2022) |
| Beschreibung: | 1 Online-Ressource (xvii, 187 Seiten) |
| ISBN: | 9781009024846 |
| DOI: | 10.1017/9781009024846 |
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| 505 | 8 | |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. | ||
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| contents | 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 |
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| dewey-full | 530.13 |
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| dewey-ones | 530 - Physics |
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| dewey-search | 530.13 |
| dewey-sort | 3530.13 |
| dewey-tens | 530 - Physics |
| discipline | Physik |
| discipline_str_mv | Physik |
| doi_str_mv | 10.1017/9781009024846 |
| format | Electronic eBook |
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| institution | BVB |
| isbn | 9781009024846 |
| language | English |
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| spelling | Abarbanel, H. D. I. 1943- (DE-588)171943899 aut The statistical physics of data assimilation and machine learning Henry D. I. Abarbanel Cambridge ; New York, NY Cambridge University Press 2022 1 Online-Ressource (xvii, 187 Seiten) txt rdacontent c rdamedia cr rdacarrier 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. Statistical physics / Data processing Discrete-time systems Supervised learning (Machine learning) / Mathematical models Stochastic processes Statistische Physik (DE-588)4057000-9 gnd rswk-swf Datenassimilation (DE-588)4803260-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Datenassimilation (DE-588)4803260-8 s Statistische Physik (DE-588)4057000-9 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-31-651963-9 https://doi.org/10.1017/9781009024846 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Abarbanel, H. D. I. 1943- The statistical physics of data assimilation and machine learning 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 Statistical physics / Data processing Discrete-time systems Supervised learning (Machine learning) / Mathematical models Stochastic processes Statistische Physik (DE-588)4057000-9 gnd Datenassimilation (DE-588)4803260-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4057000-9 (DE-588)4803260-8 (DE-588)4193754-5 |
| title | The statistical physics of data assimilation and machine learning |
| title_auth | The statistical physics of data assimilation and machine learning |
| title_exact_search | The statistical physics of data assimilation and machine learning |
| title_exact_search_txtP | The statistical physics of data assimilation and machine learning |
| title_full | The statistical physics of data assimilation and machine learning Henry D. I. Abarbanel |
| title_fullStr | The statistical physics of data assimilation and machine learning Henry D. I. Abarbanel |
| title_full_unstemmed | The statistical physics of data assimilation and machine learning Henry D. I. Abarbanel |
| title_short | The statistical physics of data assimilation and machine learning |
| title_sort | the statistical physics of data assimilation and machine learning |
| topic | Statistical physics / Data processing Discrete-time systems Supervised learning (Machine learning) / Mathematical models Stochastic processes Statistische Physik (DE-588)4057000-9 gnd Datenassimilation (DE-588)4803260-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Statistical physics / Data processing Discrete-time systems Supervised learning (Machine learning) / Mathematical models Stochastic processes Statistische Physik Datenassimilation Maschinelles Lernen |
| url | https://doi.org/10.1017/9781009024846 |
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