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:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2023
|
| Schriftenreihe: | London Mathematical Society student texts
107 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000nam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | ZDB-20-CTM-CR9781009414319 | ||
| 003 | UkCbUP | ||
| 005 | 20230731021710.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 230215s2023||||enk o ||1 0|eng|d | ||
| 020 | |a 9781009414319 | ||
| 100 | 1 | |a Sanz-Alonso, Daniel |d 1989- | |
| 245 | 1 | 0 | |a Inverse problems and data assimilation |c Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2023 | |
| 300 | |a 1 Online-Ressource (xvi, 210 Seiten) | ||
| 336 | |b txt | ||
| 337 | |b c | ||
| 338 | |b cr | ||
| 490 | 1 | |a London Mathematical Society student texts |v 107 | |
| 520 | |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. | ||
| 700 | 1 | |a Stuart, Andrew | |
| 700 | 1 | |a Taeb, Armeen | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781009414296 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781009414326 |
| 856 | 4 | 0 | |l TUM01 |p ZDB-20-CTM |q TUM_PDA_CTM |u https://doi.org/10.1017/9781009414319 |3 Volltext |
| 912 | |a ZDB-20-CTM | ||
| 912 | |a ZDB-20-CTM | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-20-CTM-CR9781009414319 |
|---|---|
| _version_ | 1818779678930894848 |
| adam_text | |
| any_adam_object | |
| author | Sanz-Alonso, Daniel 1989- |
| author2 | Stuart, Andrew Taeb, Armeen |
| author2_role | |
| author2_variant | a s as a t at |
| author_facet | Sanz-Alonso, Daniel 1989- Stuart, Andrew Taeb, Armeen |
| author_role | |
| author_sort | Sanz-Alonso, Daniel 1989- |
| author_variant | d s a dsa |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-20-CTM |
| doi_str_mv | 10.1017/9781009414319 |
| format | eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01879nam a2200289 i 4500</leader><controlfield tag="001">ZDB-20-CTM-CR9781009414319</controlfield><controlfield tag="003">UkCbUP</controlfield><controlfield tag="005">20230731021710.0</controlfield><controlfield tag="006">m|||||o||d||||||||</controlfield><controlfield tag="007">cr||||||||||||</controlfield><controlfield tag="008">230215s2023||||enk o ||1 0|eng|d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781009414319</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sanz-Alonso, Daniel</subfield><subfield code="d">1989-</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Inverse problems and data assimilation</subfield><subfield code="c">Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xvi, 210 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">London Mathematical Society student texts</subfield><subfield code="v">107</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stuart, Andrew</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taeb, Armeen</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781009414296</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781009414326</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">TUM01</subfield><subfield code="p">ZDB-20-CTM</subfield><subfield code="q">TUM_PDA_CTM</subfield><subfield code="u">https://doi.org/10.1017/9781009414319</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CTM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CTM</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-20-CTM-CR9781009414319 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T12:04:26Z |
| institution | BVB |
| isbn | 9781009414319 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xvi, 210 Seiten) |
| psigel | ZDB-20-CTM |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | London Mathematical Society student texts |
| spelling | Sanz-Alonso, Daniel 1989- Inverse problems and data assimilation Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb Cambridge Cambridge University Press 2023 1 Online-Ressource (xvi, 210 Seiten) txt c cr London Mathematical Society student texts 107 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. Stuart, Andrew Taeb, Armeen Erscheint auch als Druck-Ausgabe 9781009414296 Erscheint auch als Druck-Ausgabe 9781009414326 TUM01 ZDB-20-CTM TUM_PDA_CTM https://doi.org/10.1017/9781009414319 Volltext |
| spellingShingle | Sanz-Alonso, Daniel 1989- Inverse problems and data assimilation |
| title | Inverse problems and data assimilation |
| title_auth | Inverse problems and data assimilation |
| title_exact_search | Inverse problems and data assimilation |
| title_full | Inverse problems and data assimilation Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb |
| title_fullStr | Inverse problems and data assimilation Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb |
| title_full_unstemmed | Inverse problems and data assimilation Daniel Sanz-Alonso, Andrew Stuart, Armeen Taeb |
| title_short | Inverse problems and data assimilation |
| title_sort | inverse problems and data assimilation |
| url | https://doi.org/10.1017/9781009414319 |
| work_keys_str_mv | AT sanzalonsodaniel inverseproblemsanddataassimilation AT stuartandrew inverseproblemsanddataassimilation AT taebarmeen inverseproblemsanddataassimilation |