Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Contemporary Experimental Design, Multivariate Analysis and Data Mining : Festschrift in Honour of Professor Kai-Tai Fang / Jianqing Fan, Jianxin Pan editors
| Contemporary Experimental Design, Multivariate Analysis and Data Mining : Festschrift in Honour of Professor Kai-Tai Fang / Jianqing Fan, Jianxin Pan editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xvii, 386 p. : ill. ; 24 cm |
| Soggetto topico |
62H12 - Estimation in multivariate analysis [MSC 2020]
00B30 - Festschriften [MSC 2020] 62-XX - Statistics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Big Data
Composite design Covariance matrix Data Mining Experimental design Functional data High-Dimensional Data Longitudinal data Machine learning Multivariate Data Network data Quantile regression Robust Design Survival data Variable Selection |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0248857 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Contemporary Experimental Design, Multivariate Analysis and Data Mining : Festschrift in Honour of Professor Kai-Tai Fang / Jianqing Fan, Jianxin Pan editors
| Contemporary Experimental Design, Multivariate Analysis and Data Mining : Festschrift in Honour of Professor Kai-Tai Fang / Jianqing Fan, Jianxin Pan editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xvii, 386 p. : ill. ; 24 cm |
| Soggetto topico |
00B30 - Festschriften [MSC 2020]
62-XX - Statistics [MSC 2020] 62H12 - Estimation in multivariate analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Big Data
Composite design Covariance matrix Data Mining Experimental design Functional data High-Dimensional Data Longitudinal data Machine learning Multivariate Data Network data Quantile regression Robust Design Survival data Variable Selection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00248857 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Mastering Uncertainty in Mechanical Engineering
| Mastering Uncertainty in Mechanical Engineering |
| Autore | Pelz Peter F |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (483 p.) |
| Altri autori (Persone) |
GrochePeter
PfetschMarc E SchaeffnerMaximilian |
| Collana | Springer Tracts in Mechanical Engineering |
| Soggetto topico |
Technical design
Operational research |
| Soggetto non controllato |
Stochastic Data Uncertainty
Model Uncertainty Structural Uncertainty Robust Optimization Under Uncertainty Adaptive Technical Systems Optimal Design of Technical Systems Resilient Technical Systems Robust Design Product Design Under Uncertainty Visualization of Uncertainty Sonderforschungsbereich (SFB) 805 Fluid Dynamic Vibration Absorber 3D Servo Press Active Air Spring Active/Semi-Active Systems Increasing Flexibility in Manufacturing Open Access Book |
| ISBN | 3-030-78354-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 Motivation -- 1.2 Holistic Control of Uncertainty over the Phases of the Product Life Cycle -- 1.3 Components are Represented in Models -- 1.4 Data and Data Sources -- 1.5 Component Structures -- 1.6 Sustainable Systems Design-The Extended Motivation for This Book -- 1.7 Outlook on the Following Book Structure -- References -- 2 Types of Uncertainty -- 2.1 Data Uncertainty -- 2.1.1 Introduction -- 2.1.2 Stochastic Data Uncertainty -- 2.1.3 Incertitude -- 2.2 Model Uncertainty
2.2.1 Functional Relations, Scope and Complexity of Mathematical Models -- 2.2.2 Approaches to Detect, Quantify, and Master Model Uncertainty -- 2.3 Structural Uncertainty -- References -- 3 Our Specific Approach on Mastering Uncertainty -- 3.1 Beyond Existing Approaches -- 3.2 Uncertainty Propagation Through Process Chains -- 3.3 Five Complementary Methods for Mastering Uncertainty in Process Chains -- 3.4 Time-Variant, Dynamic and Active Processes -- 3.5 Strategies for Mastering Uncertainty-Robustness, Flexibility, Resilience -- 3.6 Exemplary Technical System Mastering Uncertainty 3.6.1 Modular Active Spring-Damper System -- 3.6.2 Active Air Spring -- 3.6.3 3D Servo Press -- References -- 4 Analysis, Quantification and Evaluation of Uncertainty -- 4.1 Identification of Uncertainty During Modelling of Technical Processes -- 4.1.1 Analysis of Data Uncertainty Using the Example of Passive and Active Vibration Isolation -- 4.1.2 Bayesian Inference Based Parameter Calibration for a Mathematical Model of a Load-Bearing Structure -- 4.1.3 Model-Based Analysis of Uncertainty in Chained Machining Processes -- 4.2 Data-Induced Conflicts 4.2.1 Dealing with Data-Induced Conflicts in Technical Systems -- 4.2.2 Data-Induced Conflicts for Wear Detection in Hydraulic Systems -- 4.2.3 Fault Detection in a Structural System -- 4.3 Analysis, Quantification and Evaluation of Model Uncertainty -- 4.3.1 Detection of Model Uncertainty via Parameter Estimation and Optimum Experimental Design -- 4.3.2 Detection of Model Uncertainty in Mathematical Models of the 3D Servo Press -- 4.3.3 Assessment of Model Uncertainty for the Modular Active Spring-Damper System -- 4.3.4 Model Uncertainty in Hardware-in-the-loop Tests 4.3.5 Identification of Model Uncertainty in the Development of Adsorption Based Hydraulic Accumulators -- 4.3.6 Uncertainty Scaling-Propagation from a Real Model to a Full-Scale System -- 4.3.7 Improvement of Surrogate Models Using Observed Data -- 4.3.8 Uncertainty Quantification with Estimated Distribution of Input Parameters -- 4.4 Representation and Visualisation of Uncertainty -- 4.4.1 Ontology-Based Information Model -- 4.4.2 Visualisation of Geometric Uncertainty in CAD Systems -- 4.4.3 Digital Twin of Load Carrying Structures for the Mastering of Uncertainty -- References 5 Methods and Technologies for Mastering Uncertainty |
| Record Nr. | UNINA-9910503009503321 |
Pelz Peter F
|
||
| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Uncertainty quantification : an accelerated course with advanced applications in computational engineering / Christian Soize
| Uncertainty quantification : an accelerated course with advanced applications in computational engineering / Christian Soize |
| Autore | Soize, Christian |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxii, 329 p. : ill. ; 24 cm |
| Soggetto topico |
47-XX - Operator theory [MSC 2020]
35-XX - Partial differential equations [MSC 2020] 37-XX - Dynamical systems and ergodic theory [MSC 2020] 60-XX - Probability theory and stochastic processes [MSC 2020] 15-XX - Linear and multilinear algebra; matrix theory [MSC 2020] 62-XX - Statistics [MSC 2020] |
| Soggetto non controllato |
High Stochastic Dimension
MCMC Methods Maximum Entropy Principle Model Uncertainties Model-parameter Uncertainties Non-Gaussian Random Fields Nonparametric Uncertainties Polynomial Chaos Expansion Random matrices Robust Design Statistical inverse problems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123409 |
|
Soize, Christian
|
||
| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Uncertainty quantification : an accelerated course with advanced applications in computational engineering / Christian Soize
| Uncertainty quantification : an accelerated course with advanced applications in computational engineering / Christian Soize |
| Autore | Soize, Christian |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxii, 329 p. : ill. ; 24 cm |
| Soggetto topico |
15-XX - Linear and multilinear algebra; matrix theory [MSC 2020]
35-XX - Partial differential equations [MSC 2020] 37-XX - Dynamical systems and ergodic theory [MSC 2020] 47-XX - Operator theory [MSC 2020] 60-XX - Probability theory and stochastic processes [MSC 2020] 62-XX - Statistics [MSC 2020] |
| Soggetto non controllato |
High Stochastic Dimension
MCMC Methods Maximum Entropy Principle Model Uncertainties Model-parameter Uncertainties Non-Gaussian Random Fields Nonparametric Uncertainties Polynomial Chaos Expansion Random matrices Robust Design Statistical inverse problems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123409 |
|
Soize, Christian
|
||
| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||