Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences
This book is thesecond volume of a three volume series recording the Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational proc...
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| 格式: | Online |
| 語言: | 英语 |
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De Gruyter
2021
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| 在線閱讀: | 33202 |
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| _version_ | 1869531281239310336 |
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| author | Freitag, Melina A Scheichl, Robert Cullen, Mike Kindermann, Stefan |
| author_browse | Cullen, Mike Freitag, Melina A Kindermann, Stefan Scheichl, Robert |
| author_facet | Freitag, Melina A Scheichl, Robert Cullen, Mike Kindermann, Stefan |
| author_sort | Freitag, Melina A |
| collection | Directory of Open Access Books |
| description | This book is thesecond volume of a three volume series recording the Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basi |
| format | Online |
| id | doab-20.500.12854ir-51417 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | De Gruyter |
| publisherStr | De Gruyter |
| record_format | ojs |
| spelling | doab-20.500.12854ir-514172023-12-20T18:40:38Z Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences Freitag, Melina A Scheichl, Robert Cullen, Mike Kindermann, Stefan QA1-939 Geosciences Data Assimilation Regularization Ill-Posed Inverse Problems Optimization bic Book Industry Communication::P Mathematics & science This book is thesecond volume of a three volume series recording the Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basi 2021-02-11T17:26:21Z 2021-02-11T17:26:21Z 2019-04-25 11:21:03 2013 book 33202 18653707 9783110282269 https://directory.doabooks.org/handle/20.500.12854/51417 eng Radon Series on Computational and Applied Mathematics image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://doi.org/10.1515/9783110282269 De Gruyter 10.1515/9783110282269 10.1515/9783110282269 af2fbfcc-ee87-43d8-a035-afb9d7eef6a5 969f21b5-ac00-4517-9de2-44973eec6874 9783110282269 212 102369 Knowledge Unlatched open access |
| spellingShingle | QA1-939 Geosciences Data Assimilation Regularization Ill-Posed Inverse Problems Optimization bic Book Industry Communication::P Mathematics & science Freitag, Melina A Scheichl, Robert Cullen, Mike Kindermann, Stefan Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title | Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title_full | Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title_fullStr | Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title_full_unstemmed | Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title_short | Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences |
| title_sort | large scale inverse problems computational methods and applications in the earth sciences |
| topic | QA1-939 Geosciences Data Assimilation Regularization Ill-Posed Inverse Problems Optimization bic Book Industry Communication::P Mathematics & science |
| topic_facet | QA1-939 Geosciences Data Assimilation Regularization Ill-Posed Inverse Problems Optimization bic Book Industry Communication::P Mathematics & science |
| url | 33202 |
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