LEADER 01215nam0 22002891i 450 001 UON00469047 005 20231205105156.220 100 $a20160905d1931 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aˆLe ‰vite del Gran capitano e del Marchese di Pescara$fPaolo Giovio$gvolgarizzate da Ludovico Domenichi$ga cura di Costantino Panigada 210 $aBari$cLaterza & FIgli$d1931 215 $a541 p.$d23 cm. 316 $avalore stimato$5IT-UONSI ITAIV/0030 410 1$1001UON00252246$12001 $aScrittori d'Italia$1210 $aBari$cLaterza & Figli 620 $aIT$dBari$3UONL000072 700 1$aGIOVIO$bPaolo$3UONV164260$0440498 702 1$aDOMENICHI$bLudovico$3UONV232302 702 1$aPANIGADA$bCostantino$3UONV178919 712 $aLaterza & Figli$3UONV276725$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00469047 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI ITA IV 0030 $eSI MR 2483 7 0030 valore stimato 996 $aVite del Gran Capitano e del Marchese di Pescara$9944467 997 $aUNIOR LEADER 05305nam 2200625 a 450 001 9911019089703321 005 20200520144314.0 010 $a1-118-60015-0 010 $a1-118-60002-9 035 $a(CKB)2670000000336677 035 $a(EBL)1124319 035 $a(SSID)ssj0000853121 035 $a(PQKBManifestationID)11498979 035 $a(PQKBTitleCode)TC0000853121 035 $a(PQKBWorkID)10866337 035 $a(PQKB)10483587 035 $a(MiAaPQ)EBC1124319 035 $a(PPN)22154836X 035 $a(OCoLC)829243605 035 $a(EXLCZ)992670000000336677 100 $a20100318d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultidisciplinary design optimization in computational mechanics /$fedited by Piotr Breitkopf, Rajan Filomeno Coelho 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2010 215 $a1 online resource (573 p.) 225 1 $aISTE 300 $aDescription based upon print version of record. 311 $a1-84821-138-4 320 $aIncludes bibliographical references and index. 327 $aCover; Multidisciplinary Design Optimization in Computational Mechanics; Title Page; Copyright Page; Table of Contents; Foreword; Notes for Instructors; Acknowledgements; Chapter 1. Multilevel Multidisciplinary Optimization in Airplane Design; 1.1. Introduction; 1.2. Overview of the traditional airplane design process and expected MDO contributions; 1.3. First step toward MDO: local dimensioning by mathematical optimization; 1.4. Second step toward MDO: multilevel multidisciplinary dimensioning; 1.5. Elements of an MDO process; 1.6. Choice of optimizers; 1.6.1. Deterministic algorithms 327 $a1.6.2. Stochastic algorithms1.7. Coupling between levels; 1.7.1. Reduction of mathematical models; 1.7.2. Simplified physical models; 1.8. Post-processing; 1.8.1. Lagrange multipliers; 1.8.2. Pareto fronts; 1.8.3. Self-organizing maps; 1.9. Conclusion; Chapter 2. Response Surface Methodology and Reduced Order Models; 2.1. Introduction; 2.2. Introducing some more notations; 2.3. Linear regression; 2.3.1. Introduction to linear regression; 2.3.2. Leverage; 2.3.3. Generalized linear regression; 2.3.4. An implicit reduced order model: moving least-squares (MLS) method 327 $a2.3.5. Bias-variance trade-off2.4. Non-linear regression; 2.4.1. Neural networks as an example of non-linear models; 2.4.2. Another example of a non-linear model: parametrized RBFs; 2.4.3. Gradient algorithms; 2.4.4. Second-order methods; 2.5. Kriging interpolation; 2.5.1. Recall on Gaussian regression; 2.5.2. Basic principles of kriging algorithms; 2.5.3. Trend estimation; 2.5.4. Covariance estimation; 2.6. Non-parametric regression and kernel-based methods; 2.6.1. Introduction to non-parametric methods; 2.6.2. Parzen window regression; 2.6.3. Radial basis functions (RBFs) 327 $a2.6.4. EM estimation of a mixture2.6.5. How RBFs are used in this book; 2.7. Support vector regression; 2.7.1. Variational formulation of SVR; 2.7.2. Dual formulation of SVR; 2.7.3. Computation of SVR models; 2.7.4. Self-reproducing Hilbert space; 2.7.5. Regularizing properties of the kernel; 2.7.6. Margin selection and ?-regression; 2.7.7. Large databases and recursive learning; 2.8. Model selection; 2.8.1. Estimating generalization error; 2.8.2. Cross-validation methods; 2.8.3. Leverage methods; 2.9. Introduction to design of computer experiments (DoCE); 2.9.1. Classical techniques 327 $a2.9.2. Input space sampling2.9.3. Adaptive learning and sequential design; 2.10. Bibliography; Chapter 3. PDE Metamodeling using Principal Component Analysis; 3.1. Principal component analysis (PCA); 3.2. Truncation rank and projector error; 3.3. Application: POD reduction of velocity fields in an engine combustion chamber; 3.4. Reduced-basis methods, numerical analysis; 3.4.1. POD-Galerkin projection method; 3.4.2. Dual approach POD-Petrov-Galerkin; 3.5. Intrusive/non-intrusive aspects; 3.6. Double reduction in both space and parameter dimensions; 3.7. The weighted residual method 327 $a3.8. Non-linear problems 330 $aThis book provides a comprehensive introduction to the mathematical and algorithmic methods for the Multidisciplinary Design Optimization (MDO) of complex mechanical systems such as aircraft or car engines. We have focused on the presentation of strategies efficiently and economically managing the different levels of complexity in coupled disciplines (e.g. structure, fluid, thermal, acoustics, etc.), ranging from Reduced Order Models (ROM) to full-scale Finite Element (FE) or Finite Volume (FV) simulations. Particular focus is given to the uncertainty quantification and its impact on the robus 410 0$aISTE 606 $aEngineering design 606 $aEngineering mathematics 615 0$aEngineering design. 615 0$aEngineering mathematics. 676 $a621.8/15 701 $aBreitkopf$b Piotr$0918057 701 $aCoelho$b Rajan Filomeno$01838247 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019089703321 996 $aMultidisciplinary design optimization in computational mechanics$94417201 997 $aUNINA