LEADER 01790nam 2200529I 450 001 9910705483103321 005 20140604135717.0 035 $a(CKB)5470000002450553 035 $a(OCoLC)880940245 035 $a(EXLCZ)995470000002450553 100 $a20140604j201401 ua 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNASA AVOSS fast-time wake prediction models $euser's guide /$fNash'at N. Ahmad and Randal L. VanValkenburg, Matthew Pruis 210 1$aHampton, Virginia :$cNational Aeronautics and Space Administration, Langley Research Center,$dJanuary 2014. 215 $a1 online resource (iv, 19 pages) $ccolor illustrations 225 1 $aNASA/TM ;$v2014-218152 300 $aTitle from title screen (viewed June 4, 2014). 300 $a"January 2014." 517 $aNASA AVOSS fast-time wake prediction models 606 $aAircraft approach spacing$2nasat 606 $aCurrent distribution$2nasat 606 $aEnergy dissipation$2nasat 606 $aModels$2nasat 606 $aMeteorology$2nasat 606 $aNASA programs$2nasat 606 $aNational Airspace System$2nasat 615 7$aAircraft approach spacing. 615 7$aCurrent distribution. 615 7$aEnergy dissipation. 615 7$aModels. 615 7$aMeteorology. 615 7$aNASA programs. 615 7$aNational Airspace System. 700 $aAhmad$b Nash'at N.$01397882 702 $aVanValkenburg$b Randal L. 702 $aPruis$b Matthew 712 02$aLangley Research Center, 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910705483103321 996 $aNASA AVOSS fast-time wake prediction models$93504390 997 $aUNINA LEADER 06942nam 22006135 450 001 9910254090303321 005 20200701165631.0 010 $a3-319-34120-0 024 7 $a10.1007/978-3-319-34120-0 035 $a(CKB)3710000001006475 035 $a(DE-He213)978-3-319-34120-0 035 $a(MiAaPQ)EBC6311621 035 $a(MiAaPQ)EBC5610859 035 $a(Au-PeEL)EBL5610859 035 $a(OCoLC)974295863 035 $a(PPN)197455476 035 $a(EXLCZ)993710000001006475 100 $a20161223d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFrom Lévy-Type Processes to Parabolic SPDEs /$fby Davar Khoshnevisan, René Schilling ; edited by Frederic Utzet, Lluis Quer-Sardanyons 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2016. 215 $a1 online resource (VIII, 219 p.) 225 1 $aAdvanced Courses in Mathematics - CRM Barcelona,$x2297-0304 300 $aIncludes index. 311 $a3-319-34119-7 327 $aIntro -- Contents -- Part I: An Introduction to Le?vy and Feller Processes -- Preface -- Symbols and Notation -- Chapter 1: Orientation -- Chapter 2: Le?vy Processes -- Chapter 3: Examples -- Chapter 4: On the Markov Property -- Chapter 5: A Digression: Semigroups -- Chapter 6: The Generator of a Le?vy Process -- Chapter 7: Construction of Le?vy Processes -- Chapter 8: Two Special Le?vy Processes -- Chapter 9: Random Measures -- Chapter 10: A Digression: Stochastic Integrals -- Chapter 11: From Le?vy to Feller Processes -- Chapter 12: Symbols and Semimartingales -- Chapter 13: De?nouement -- Appendix: Some Classical Results -- The Cauchy-Abel functional equation -- Characteristic functions and moments -- Vague and weak convergence of measures -- Convergence in distribution -- The predictable ?-algebra -- The structure of translation invariant operators -- Bibliography -- Part II: Invariance and Comparison Principles for Parabolic Stochastic Partial Differential Equations -- Preface -- Chapter 14: White Noise -- 14.1 Some heuristics -- 14.2 LCA groups -- 14.3 White noise on G -- 14.4 Space-time white noise -- 14.5 The Walsh stochastic integral -- 14.5.1 Simple random fields -- 14.5.2 Elementary random fields -- 14.5.3 Walsh-integrable random fields -- 14.6 Moment inequalities -- 14.7 Examples of Walsh-integrable random fields -- 14.7.1 Integral kernels -- 14.7.2 Stochastic convolutions -- 14.7.3 Relation to Ito? integrals -- Chapter 15: Le?vy Processes -- 15.1 Introduction -- 15.1.1 Le?vy processes on R -- 15.1.2 Le?vy processes on T -- 15.1.3 Le?vy processes on Z -- 15.1.4 Le?vy processes on Z/nZ -- 15.2 The semigroup -- 15.3 The Kolmogorov-Fokker-Planck equation -- 15.3.1 Le?vy processes on R -- Chapter 16: SPDEs -- 16.1 A heat equation -- 16.2 A parabolic SPDE -- 16.2.1 Le?vy processes on R -- 16.2.2 Le?vy processes on a denumerable LCA group. 327 $a16.2.3 Proof of Theorem 16.2.2 -- 16.3 Examples -- 16.3.1 The trivial group -- 16.3.2 The cyclic group on two elements -- 16.3.3 The integer group -- 16.3.4 The additive reals -- 16.3.5 Higher dimensions -- Chapter 17: An Invariance Principle for Parabolic SPDEs -- 17.1 A central limit theorem -- 17.2 A local central limit theorem -- 17.3 Particle systems -- Chapter 18: Comparison Theorems -- 18.1 Positivity -- 18.2 The Cox-Fleischmann-Greven inequality -- 18.3 Slepian's inequality -- Chapter 19: A Dash of Color -- 19.1 Reproducing kernel Hilbert spaces -- 19.2 Colored noise -- 19.2.1 Example: white noise -- 19.2.2 Example: Hilbert-Schmidt covariance -- 19.2.3 Example: spatially-homogeneous covariance -- 19.2.4 Example: tensor-product covariance -- 19.3 Linear SPDEs with colored-noise forcing -- Bibliography -- Index. 330 $aThis volume presents the lecture notes from two courses given by Davar Khoshnevisan and René Schilling, respectively, at the second Barcelona Summer School on Stochastic Analysis. René Schilling?s notes are an expanded version of his course on Lévy and Lévy-type processes, the purpose of which is two-fold: on the one hand, the course presents in detail selected properties of the Lévy processes, mainly as Markov processes, and their different constructions, eventually leading to the celebrated Lévy-Itô decomposition. On the other, it identifies the infinitesimal generator of the Lévy process as a pseudo-differential operator whose symbol is the characteristic exponent of the process, making it possible to study the properties of Feller processes as space inhomogeneous processes that locally behave like Lévy processes. The presentation is self-contained, and includes dedicated chapters that review Markov processes, operator semigroups, random measures, etc. In turn, Davar Khoshnevisan?s course investigates selected problems in the field of stochastic partial differential equations of parabolic type. More precisely, the main objective is to establish an Invariance Principle for those equations in a rather general setting, and to deduce, as an application, comparison-type results. The framework in which these problems are addressed goes beyond the classical setting, in the sense that the driving noise is assumed to be a multiplicative space-time white noise on a group, and the underlying elliptic operator corresponds to a generator of a Lévy process on that group. This implies that stochastic integration with respect to the above noise, as well as the existence and uniqueness of a solution for the corresponding equation, become relevant in their own right. These aspects are also developed and supplemented by a wealth of illustrative examples. 410 0$aAdvanced Courses in Mathematics - CRM Barcelona,$x2297-0304 606 $aProbabilities 606 $aDifferential equations, Partial 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aPartial Differential Equations$3https://scigraph.springernature.com/ontologies/product-market-codes/M12155 615 0$aProbabilities. 615 0$aDifferential equations, Partial. 615 14$aProbability Theory and Stochastic Processes. 615 24$aPartial Differential Equations. 676 $a519.2 700 $aKhoshnevisan$b Davar$4aut$4http://id.loc.gov/vocabulary/relators/aut$0318960 702 $aSchilling$b René$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aUtzet$b Frederic$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aQuer-Sardanyons$b Lluis$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254090303321 996 $aFrom Lévy-Type Processes to Parabolic SPDEs$92039115 997 $aUNINA