Inverse problems and data assimilation:
This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underp...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York, NY ; Port Melbourne ; New Delhi ; Singapore
Cambridge University Press
2023
|
| Schriftenreihe: | London Mathematical Society student texts
107 |
| Schlagworte: | |
| Zusammenfassung: | This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underpinnings of methodologies widely used in their disciplines. The authors examine inverse problems and data assimilation in turn, before exploring the use of data assimilation methods to solve generic inverse problems by introducing an artificial algorithmic time. Topics covered include maximum a posteriori estimation, (stochastic) gradient descent, variational Bayes, Monte Carlo, importance sampling and Markov chain Monte Carlo for inverse problems; and 3DVAR, 4DVAR, extended and ensemble Kalman filters, and particle filters for data assimilation. The book contains a wealth of examples and exercises, and can be used to accompany courses as well as for self-study. |
| Beschreibung: | xvi, 210 Seiten Diagramme |
| ISBN: | 9781009414326 9781009414296 |
Internformat
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| 245 | 1 | 0 | |a Inverse problems and data assimilation |c Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington) |
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| 505 | 8 | |a Bayesian inverse problems and well-posedness -- The Linear-Gaussian setting -- Optimization perspective -- Gaussian approximation -- Monte Carlo sampling and importance sampling -- Markov Chain Monte Carlo -- Filtering and smoothing problems and well-posedness -- The Linear-Gaussian setting -- Optimization for filtering and smoothing: 3DVAR and 4DVAR -- The extended and ensemble Kalman filters -- Particle filter -- Optimal particle filter -- Blending inverse problems and data assimilation. | |
| 520 | 3 | |a This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underpinnings of methodologies widely used in their disciplines. The authors examine inverse problems and data assimilation in turn, before exploring the use of data assimilation methods to solve generic inverse problems by introducing an artificial algorithmic time. Topics covered include maximum a posteriori estimation, (stochastic) gradient descent, variational Bayes, Monte Carlo, importance sampling and Markov chain Monte Carlo for inverse problems; and 3DVAR, 4DVAR, extended and ensemble Kalman filters, and particle filters for data assimilation. The book contains a wealth of examples and exercises, and can be used to accompany courses as well as for self-study. | |
| 650 | 0 | 7 | |a Mathematisches Problem |0 (DE-588)4114530-6 |2 gnd |9 rswk-swf |
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| 650 | 0 | 7 | |a Inverses Problem |0 (DE-588)4125161-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenassimilation |0 (DE-588)4803260-8 |2 gnd |9 rswk-swf |
| 653 | |a nonlinear filtering | ||
| 653 | |a Monte Carlo Methoden | ||
| 653 | |a Variational Bayes | ||
| 653 | |a meteorologische Datenassimilation | ||
| 653 | 0 | |a Angewandte Informatik | |
| 653 | 0 | |a COMPUTERS / General | |
| 653 | 0 | |a Datenwissenschaft und -analyse: allgemein | |
| 653 | 0 | |a Informationstheorie | |
| 653 | 0 | |a Maschinelles Lernen | |
| 653 | 0 | |a Meteorologie und Klimatologie (Klimaforschung) | |
| 653 | 0 | |a Numerical analysis | |
| 653 | 0 | |a Theoretische Informatik | |
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| 700 | 1 | |a Taeb, Armeen |e Verfasser |4 aut | |
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| 830 | 0 | |a London Mathematical Society student texts |v 107 |w (DE-604)BV000841726 |9 107 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-034585685 | ||
Datensatz im Suchindex
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| author | Sanz-Alonso, Daniel Stuart, Andrew Taeb, Armeen |
| author_facet | Sanz-Alonso, Daniel Stuart, Andrew Taeb, Armeen |
| author_role | aut aut aut |
| author_sort | Sanz-Alonso, Daniel |
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| bvnumber | BV049324808 |
| contents | Bayesian inverse problems and well-posedness -- The Linear-Gaussian setting -- Optimization perspective -- Gaussian approximation -- Monte Carlo sampling and importance sampling -- Markov Chain Monte Carlo -- Filtering and smoothing problems and well-posedness -- The Linear-Gaussian setting -- Optimization for filtering and smoothing: 3DVAR and 4DVAR -- The extended and ensemble Kalman filters -- Particle filter -- Optimal particle filter -- Blending inverse problems and data assimilation. |
| ctrlnum | (OCoLC)1403381066 (DE-599)KXP1854608630 |
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| id | DE-604.BV049324808 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:44:00Z |
| indexdate | 2024-07-10T10:01:35Z |
| institution | BVB |
| isbn | 9781009414326 9781009414296 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034585685 |
| oclc_num | 1403381066 |
| open_access_boolean | |
| owner | DE-20 DE-188 |
| owner_facet | DE-20 DE-188 |
| physical | xvi, 210 Seiten Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Cambridge University Press |
| record_format | marc |
| series | London Mathematical Society student texts |
| series2 | London Mathematical Society student texts |
| spelling | Sanz-Alonso, Daniel Verfasser aut Inverse problems and data assimilation Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington) Cambridge ; New York, NY ; Port Melbourne ; New Delhi ; Singapore Cambridge University Press 2023 xvi, 210 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier London Mathematical Society student texts 107 Bayesian inverse problems and well-posedness -- The Linear-Gaussian setting -- Optimization perspective -- Gaussian approximation -- Monte Carlo sampling and importance sampling -- Markov Chain Monte Carlo -- Filtering and smoothing problems and well-posedness -- The Linear-Gaussian setting -- Optimization for filtering and smoothing: 3DVAR and 4DVAR -- The extended and ensemble Kalman filters -- Particle filter -- Optimal particle filter -- Blending inverse problems and data assimilation. This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underpinnings of methodologies widely used in their disciplines. The authors examine inverse problems and data assimilation in turn, before exploring the use of data assimilation methods to solve generic inverse problems by introducing an artificial algorithmic time. Topics covered include maximum a posteriori estimation, (stochastic) gradient descent, variational Bayes, Monte Carlo, importance sampling and Markov chain Monte Carlo for inverse problems; and 3DVAR, 4DVAR, extended and ensemble Kalman filters, and particle filters for data assimilation. The book contains a wealth of examples and exercises, and can be used to accompany courses as well as for self-study. Mathematisches Problem (DE-588)4114530-6 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 gnd rswk-swf Numerische Wettervorhersage (DE-588)4268740-8 gnd rswk-swf Inverses Problem (DE-588)4125161-1 gnd rswk-swf Datenassimilation (DE-588)4803260-8 gnd rswk-swf nonlinear filtering Monte Carlo Methoden Variational Bayes meteorologische Datenassimilation Angewandte Informatik COMPUTERS / General Datenwissenschaft und -analyse: allgemein Informationstheorie Maschinelles Lernen Meteorologie und Klimatologie (Klimaforschung) Numerical analysis Theoretische Informatik Datenassimilation (DE-588)4803260-8 s Kalman-Filter (DE-588)4130759-8 s Inverses Problem (DE-588)4125161-1 s Numerische Wettervorhersage (DE-588)4268740-8 s Mathematisches Problem (DE-588)4114530-6 s DE-604 Stuart, Andrew Verfasser aut Taeb, Armeen Verfasser aut 9781009414319 ebook London Mathematical Society student texts 107 (DE-604)BV000841726 107 |
| spellingShingle | Sanz-Alonso, Daniel Stuart, Andrew Taeb, Armeen Inverse problems and data assimilation London Mathematical Society student texts Bayesian inverse problems and well-posedness -- The Linear-Gaussian setting -- Optimization perspective -- Gaussian approximation -- Monte Carlo sampling and importance sampling -- Markov Chain Monte Carlo -- Filtering and smoothing problems and well-posedness -- The Linear-Gaussian setting -- Optimization for filtering and smoothing: 3DVAR and 4DVAR -- The extended and ensemble Kalman filters -- Particle filter -- Optimal particle filter -- Blending inverse problems and data assimilation. Mathematisches Problem (DE-588)4114530-6 gnd Kalman-Filter (DE-588)4130759-8 gnd Numerische Wettervorhersage (DE-588)4268740-8 gnd Inverses Problem (DE-588)4125161-1 gnd Datenassimilation (DE-588)4803260-8 gnd |
| subject_GND | (DE-588)4114530-6 (DE-588)4130759-8 (DE-588)4268740-8 (DE-588)4125161-1 (DE-588)4803260-8 |
| title | Inverse problems and data assimilation |
| title_auth | Inverse problems and data assimilation |
| title_exact_search | Inverse problems and data assimilation |
| title_exact_search_txtP | Inverse problems and data assimilation |
| title_full | Inverse problems and data assimilation Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington) |
| title_fullStr | Inverse problems and data assimilation Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington) |
| title_full_unstemmed | Inverse problems and data assimilation Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington) |
| title_short | Inverse problems and data assimilation |
| title_sort | inverse problems and data assimilation |
| topic | Mathematisches Problem (DE-588)4114530-6 gnd Kalman-Filter (DE-588)4130759-8 gnd Numerische Wettervorhersage (DE-588)4268740-8 gnd Inverses Problem (DE-588)4125161-1 gnd Datenassimilation (DE-588)4803260-8 gnd |
| topic_facet | Mathematisches Problem Kalman-Filter Numerische Wettervorhersage Inverses Problem Datenassimilation |
| volume_link | (DE-604)BV000841726 |
| work_keys_str_mv | AT sanzalonsodaniel inverseproblemsanddataassimilation AT stuartandrew inverseproblemsanddataassimilation AT taebarmeen inverseproblemsanddataassimilation |