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Computerized symbolic manipulation in mechanics / edited by E. Kreuzer
| Computerized symbolic manipulation in mechanics / edited by E. Kreuzer |
| Pubbl/distr/stampa | Wien [etc.] : Springer, copyr. 1994 |
| Descrizione fisica | 262 p. : ill. ; 24 cm |
| Disciplina | 620.100285 |
| Collana | Courses and lectures / International centre for mechanical sciences |
| Soggetto topico |
Meccanica applicata - Modelli matematici
Sistemi meccanici - Analisi - Impiego degli elaboratori elettronici |
| ISBN | 3-211-82616-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990000361710203316 |
| Wien [etc.] : Springer, copyr. 1994 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Multidisciplinary design optimization in computational mechanics [[electronic resource] /] / edited by Piotr Breitkopf, Rajan Filomeno Coelho
| Multidisciplinary design optimization in computational mechanics [[electronic resource] /] / edited by Piotr Breitkopf, Rajan Filomeno Coelho |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (573 p.) |
| Disciplina | 620.100285 |
| Altri autori (Persone) |
BreitkopfPiotr
CoelhoRajan Filomeno |
| Collana | ISTE |
| Soggetto topico |
Engineering design
Engineering mathematics |
| ISBN |
1-118-60015-0
1-118-60002-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; 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
1.6.2. 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 2.3.5. 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) 2.6.4. 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 ν-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 2.9.2. 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 3.8. Non-linear problems |
| Record Nr. | UNINA-9910141601803321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multidisciplinary design optimization in computational mechanics [[electronic resource] /] / edited by Piotr Breitkopf, Rajan Filomeno Coelho
| Multidisciplinary design optimization in computational mechanics [[electronic resource] /] / edited by Piotr Breitkopf, Rajan Filomeno Coelho |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (573 p.) |
| Disciplina | 620.100285 |
| Altri autori (Persone) |
BreitkopfPiotr
CoelhoRajan Filomeno |
| Collana | ISTE |
| Soggetto topico |
Engineering design
Engineering mathematics |
| ISBN |
1-118-60015-0
1-118-60002-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; 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
1.6.2. 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 2.3.5. 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) 2.6.4. 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 ν-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 2.9.2. 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 3.8. Non-linear problems |
| Record Nr. | UNINA-9910830099103321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A Numerical Approach to the Classical Laminate Theory of Composite Materials : The Composite Laminate Analysis Tool—CLAT v2.0 / / by Andreas Öchsner, Resam Makvandi
| A Numerical Approach to the Classical Laminate Theory of Composite Materials : The Composite Laminate Analysis Tool—CLAT v2.0 / / by Andreas Öchsner, Resam Makvandi |
| Autore | Öchsner Andreas |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (181 pages) |
| Disciplina | 620.100285 |
| Altri autori (Persone) | MakvandiResam |
| Collana | Advanced Structured Materials |
| Soggetto topico |
Materials science—Data processing
Continuum mechanics Composite materials Computational Materials Science Continuum Mechanics Composites |
| ISBN | 3-031-32975-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Classical Laminate Theory -- 3. Composite Laminate Analysis Tool - CLAT -- 4. Application Examples -- 5. Source Codes. |
| Record Nr. | UNINA-9910734849003321 |
Öchsner Andreas
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Phase Field Theory in Materials Physics [[electronic resource] ] : The Hodograph Equation / / by Peter Galenko
| Phase Field Theory in Materials Physics [[electronic resource] ] : The Hodograph Equation / / by Peter Galenko |
| Autore | Galenko Peter |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 620.100285 |
| Soggetto topico |
Materials science - Data processing
Condensed matter Metals Thermodynamics Mathematical physics Computational Materials Science Structure of Condensed Matter Metals and Alloys Phase Transitions and Multiphase Systems Mathematical Methods in Physics |
| ISBN | 3-031-49278-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Phase Interfaces -- Phase Field -- Gibbs Free Energy: Equilibrium and Dynamics -- Approach to Fast Transformations -- The Hodograph Equation -- Velocity-Dependent Herring Equation. |
| Record Nr. | UNINA-9910835061003321 |
| Galenko Peter | ||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||