LEADER 00677nam2 2200241 450 001 990003699660403321 005 20060102125703.0 035 $a000369966 035 $aFED01000369966 035 $a(Aleph)000369966FED01 035 $a000369966 100 $a20030910d--------km-y0itay50------ba 101 0 $aita 200 1 $a<>sistemi territoriali d'impresa nel Mezzogiorno$fGiovanni Barbieri 215 $ap. 499-592 463 0$1001000380229 700 1$aBarbieri,$bGiovanni$071551 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aAN 912 $a990003699660403321 959 $aDECSE 996 $aSistemi territoriali d'impresa nel Mezzogiorno$9499569 997 $aUNINA LEADER 05376oam 22006733u 450 001 996309233403316 005 20211216205029.0 010 $a3-11-028226-7 024 7 $a10.1515/9783110282269 035 $a(CKB)2670000000432688 035 $a(EBL)1113333 035 $a(OCoLC)858761758 035 $a(SSID)ssj0001002059 035 $a(PQKBManifestationID)11532419 035 $a(PQKBTitleCode)TC0001002059 035 $a(PQKBWorkID)10967936 035 $a(PQKB)10537107 035 $a(DE-B1597)175844 035 $a(OCoLC)857804565 035 $a(DE-B1597)9783110282269 035 $a(Au-PeEL)EBL1113333 035 $a(CaPaEBR)ebr10784108 035 $a(CaONFJC)MIL807812 035 $a(ScCtBLL)6221c84f-e90b-4035-8555-44e2a1a5b321 035 $a(MiAaPQ)EBC1113333 035 $a(EXLCZ)992670000000432688 100 $a20130429h20132013 uy 0 101 0 $aeng 135 $aurnn#---|u||u 181 $ctxt 182 $cc 183 $acr 200 00$aLarge scale inverse problems $ecomputational methods and applications in the earth sciences /$fedited by Mike Cullen[and three others] 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2013] 210 4$dİ2013 215 $a1 online resource (216 p.) 225 1 $aRadon series on computational and applied mathematics,$x1865-3707 ;$vvolume 13 300 $aDescription based upon print version of record. 311 0 $a3-11-028222-4 320 $aIncludes bibliographical references. 327 $tFront matter --$tPreface --$tContents --$tSynergy of inverse problems and data assimilation techniques /$rFreitag, Melina A. / Potthast, Roland W. E. --$tVariational data assimilation for very large environmental problems /$rLawless, Amos S. --$tEnsemble filter techniques for intermittent data assimilation /$rReich, Sebastian / Cotter, Colin J. --$tInverse problems in imaging /$rBurger, Martin / Dirks, Hendrik / Müller, Jahn --$tThe lost honor of ?2-based regularization /$rDoel, Kees van den / Ascher, Uri M. / Haber, Eldad --$tList of contributors --$tBack matter 330 $aThis book is the second 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. This collection of survey articles focusses on the 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 basic deterministic technique is to fit a model trajectory to the observations over a period of time to within the observation error. Since the model is not perfect the model trajectory has to be corrected, which defines the data assimilation problem. The stochastic view can be expressed by using an ensemble of model trajectories, and calculating corrections to both the mean value and the spread which allow the observations to be fitted by each ensemble member. In other areas of earth science, only the structure of the model formulation itself is known and the aim is to use the past observation history to determine the unknown model parameters. The book records the achievements of Workshop 2 "Large-Scale Inverse Problems and Applications in the Earth Sciences". It involves experts in the theory of inverse problems together with experts working on both theoretical and practical aspects of the techniques by which large inverse problems arise in the earth sciences. 410 0$aRadon series in computational and applied mathematics ;$v13. 606 $aInverse problems (Differential equations) 610 $aData Assimilation. 610 $aGeosciences. 610 $aIll-Posed Inverse Problems. 610 $aOptimization. 610 $aRegularization. 615 0$aInverse problems (Differential equations) 676 $a515/.357 701 $aCullen$b Michael J. P$0941204 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996309233403316 996 $aLarge scale inverse problems$92122922 997 $aUNISA