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[null,"1.","Record Nr.",null,"UNINA9910508439703321"]
[null,null,"Autore",null,"Serfaty Simon"]
[null,null,"Titolo",null,"America in the World from Truman to Biden : Play It Again, Sam"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing AG, , 2021"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-81761-X"]
[null,null,"Descrizione fisica",null,"1 online resource (197 pages)"]
[null,null,"Disciplina",null,"327.73009045"]
[null,null,"Soggetti",null,"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Acknowledgments -- Contents -- Prologue -- When History Comes -- While History Lingers -- Notes -- Part I Getting It Done,  Half a World, A Free World -- 1 Memories of Leadership -- Bound to Lead -- Half Past Europe, Half Before America -- Getting Started -- Where Have the Leaders Gone? -- Promises Kept -- Promises, Promises -- 2 Rising to Primacy -- Here to Stay -- Mapping Expectations -- First Take -- At War Abroad -- Getting in -- Getting Out -- 3 False Starts -- No Time to Come Home -- The Clinton Promise -- The Russian Lesson -- Russia Is Back -- Russia Reset -- The Threat Elsewhere -- No Exit -- We're All at War Now -- Part II Letting Go- A World Undone, A Whole World -- 4 A World Unhinged -- No Rest for the West -- Bad War, Wrong Place -- Second Take -- A Zero-Polar Moment -- Post-Wars Agenda -- Post-Secular Agenda -- Post-National Agenda -- Who Will Steal the Show? -- Power and Weaknesses -- Europe, as a Union but with the United States -- Russia, against the West but without the Rest -- An ascending China and Asia's red lines -- As Time Goes by -- 5 A World on Edge -- Faux Multipolarity -- Superpower by Improvisation -- Mapping the Power Transitions -- Crises of Apprenticeship -- The Obama Moment -- It's the World, Stupid -- 6 Make America Whole Again -- Half Past Trump -- A Quarter Before Biden -- Bibliography."]
[null,"2.","Record Nr.",null,"UNISA996466732803316"]
[null,null,"Titolo",null,"Advances in uncertainty quantification and optimization under uncertainty with aerospace applications : proceedings of the 2020 UQOP international conference / / edited by Massimiliano Vasile and Domenico Quagliarella"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-80542-5"]
[null,null,"Descrizione fisica",null,"1 online resource (448 pages)"]
[null,null,"Collana",null,"Space Technology Proceedings ; ; v.8"]
[null,null,"Disciplina",null,"629.101519544"]
[null,null,"Soggetti",null,"Measurement uncertainty (Statistics)"]
[{"$nulls":4},"Mathematical optimization"]
[{"$nulls":4},"Outer space"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Part I Applications of Uncertainty in Aerospace &amp -- Engineering (ENG) -- From Uncertainty Quantification to Shape Optimization: Cross-Fertilization of Methods for Dimensionality Reduction -- 1 Introduction -- 2 Design-Space Dimensionality Reduction in Shape Optimization -- 2.1 Geometry-Based Formulation -- 2.2 Physics-Informed Formulation -- 3 Example Application -- 4 Concluding Remarks -- References -- Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems -- Nomenclature -- 1 Introduction -- 2 Modelling of an AI-ETIPS -- 2.1 Computational Model -- 2.2 Case of Study -- 3 Cloud Uncertainty Characterization -- 4 Uncertainty Propagation Methodologies -- 4.1 Monte Carlo Sampling Methods -- 4.2 Generalized Polynomial Chaos Expansion -- 5 Numerical Results -- 6 Concluding Remarks -- References -- Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation -- 1 Introduction -- 2 Multi-fidelity Gaussian Process Regression -- 3 Aerodynamic Computational Chain -- 4 Far-Field Drag Coefficient Calculation -- 5 Deterministic Design Optimisation Problem -- 6 Probabilistic Design Optimisation Problem -- 7 Optimisation Pipeline -- 8 Results -- 8.1 Deterministic Optimisation -- 8.2 Probabilistic Optimisation -- 9 "]
[{"$nulls":4},"Conclusion -- References -- Scalable Dynamic Asynchronous Monte Carlo Framework Applied to Wind Engineering Problems -- 1 Introduction -- 2 Monte Carlo Methods -- 2.1 Monte Carlo -- 2.2 Asynchronous Monte Carlo -- 2.3 Scheduling -- 3 Wind Engineering Benchmark -- 3.1 Problem Description -- 3.2 Source of Uncertainty -- 3.3 Results -- 4 Conclusion -- References -- Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE -- 1 Introduction."]
[{"$nulls":4},"2 Methodology and Description of the Proposed Model -- 2.1 Extracting Availability and Economic Cost from Functionability Profiles -- 2.2 Multi-Objective Optimization Approach -- 2.3 Building Functionability Profiles -- 3 The Application Case -- 4 Results and Discussion -- 5 Conclusions -- References -- Multi-objective Robustness Analysis of the Polymer Extrusion Process -- 1 Introduction -- 2 Robustness in Polymer Extrusion -- 2.1 Extrusion Process -- 2.2 Robustness Methodology -- 2.3 Multi-objective Optimization with Robustness -- 3 Results and Discussion -- 4 Conclusion -- References -- Quantification of Operational and Geometrical Uncertainties of a 1.5-Stage Axial Compressor with Cavity Leakage Flows -- 1 Motivation and Test Case Description -- 1.1 Geometry and Operating Regime -- 1.2 Uncertainty Definition -- Correlated Fields at the Main Inlet -- Secondary Inlets -- Rotor Blade Tip Gap -- 2 Uncertainty Quantification Method -- 2.1 Scaled Sensitivity Derivatives -- 3 Simulation Setup and Computational Cost -- 4 Results and Discussion -- 4.1 Non-deterministic Performance Curve -- 4.2 Scaled Sensitivity Derivatives -- 5 Conclusions -- References -- Can Uncertainty Propagation Solve the Mysterious Case of Snoopy? -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Dynamics Modelling -- 3.2 Using the TDA Structure to Solve ODE -- 3.3 Performing Numerical Analysis -- 3.4 Propagator Implementation and Validation -- 3.5 Monte-Carlo Estimation -- 4 Results and Discussion -- 4.1 Performing Numerical Analysis on the Trajectory of Snoopy -- 4.2 Computing Snoopy's Trajectory -- 4.3 Estimating the Probability of Snoopy's Presence -- 5 Conclusions and Future Work -- References -- Part II Imprecise Probability, Theory and Applications (IP) -- Robust Particle Filter for Space Navigation Under EpistemicUncertainty -- 1 Introduction."]
[{"$nulls":4},"2 Filtering Under Epistemic Uncertainty -- 2.1 Imprecise Formulation -- 2.2 Expectation Estimator -- 2.3 Bound Estimator -- 3 Test Case -- 3.1 Initial State Uncertainty -- 3.2 Observation Model and Errors -- 3.3 Results -- 4 Conclusions -- References -- Computing Bounds for Imprecise Continuous-Time Markov Chains Using Normal Cones -- 1 Introduction -- 2 Imprecise Markov Chains in Continuous Time -- 2.1 Imprecise Distributions over States -- 2.2 Imprecise Transition Rate Matrices -- 2.3 Distributions at Time t -- 3 Numerical Methods for Finding Lower Expectations -- 3.1 Lower Expectation and Transition Operators as Linear Programming Problems -- 3.2 Computational Approaches to Estimating Lower Expectation Functionals -- 4 Normal Cones of Imprecise Q-Operators -- 5 Norms of Q-Matrices -- 6 Numerical Methods for CTIMC Bounds Calculation -- 6.1 Matrix Exponential Method -- 6.2 Checking Applicability of the Matrix Exponential Method -- 6.3 Checking the Normal Cone Inclusion -- 6.4 Approximate Matrix Exponential Method -- 7 Error Estimation -- 7.1 General Error Bounds -- 7.2 Error Estimation for a Single Step -- 7.3 Error Estimation for the Uniform Grid -- 8 Algorithm and Examples -- 8.1 Parts of the Algorithm -- 8.2 Examples -- 9 Concluding Remarks -- References -- Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference -- 1 Introduction -- 2 Markov Chain Monte Carlo -- 3 Simultaneous Sampling -- 4 Markov Chain Monte Carlo for "]
[{"$nulls":4},"Imprecise Models -- 5 Practical Implementation -- 6 Linear Representation for Exponential Families -- 7 Computer Representation of the Credal Sets -- 8 Credal Set Merging -- 9 Discussion -- Reference -- Computing Expected Hitting Times for Imprecise Markov Chains -- 1 Introduction -- 2 Existence of Solutions -- 3 A Computational Method -- 4 Complexity Analysis -- References."]
[{"$nulls":4},"Part III Robust and Reliability-Based Design Optimisation in Aerospace Engineering (RBDO) -- Multi-Objective Robust Trajectory Optimization of Multi-Asteroid Fly-By Under Epistemic Uncertainty -- 1 Introduction -- 2 Problem Formulation -- 3 Lower Expectation -- 3.1 Minimizing the Expectation -- 3.2 Estimating the Expectation -- 4 Multi-Objective Optimization -- 4.1 Control Mapping for Dimensionality Reduction -- Deterministic Control Map -- Max-Min Control Map -- Min-Max Control Map -- 4.2 Threshold Mapping -- 5 Asteroid Tour Test Case -- 6 Results -- 6.1 Control Map and Threshold Map -- 6.2 Lower Expectation -- 6.3 Expectation and Sampling Methods -- 6.4 Execution Times -- 7 Conclusions -- References -- Reliability-Based Robust Design Optimization of a Jet Engine Nacelle -- 1 Introduction -- 2 Definition of Aeronautical Optimization Under Uncertainties -- 2.1 Nacelle Acoustic Liner and Manufacturing Tolerances -- 2.2 Nacelle Acoustic Liner FEM Model -- 3 Adaptive Sparse Polynomial Chaos for Reliability Problems -- 3.1 Basic Formulation of Adaptive PCE -- 3.2 Adaptive Sparse Polynomial Chaos Expansion -- 3.3 Application of Adaptive PCE to Reliability-Based Optimization -- 4 Reliability-Based Optimization of the Engine Nacelle -- 4.1 Optimization Platform -- 4.2 Optimization Results -- 5 Conclusion -- References -- Bayesian Optimization for Robust Solutions Under Uncertain Input -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 4 Methodology -- 4.1 Gaussian Process -- 4.2 Robust Bayesian Optimization -- Direct Robustness Approximation -- Robust Knowledge Gradient -- 4.3 Stochastic Kriging -- 5 Experiments -- 5.1 Benchmark Problems -- Test Functions -- Experimental Setup -- 5.2 Results -- Latin Hypercube Sampling -- Stochastic Kriging -- Uncontrollable Input -- 6 Conclusions -- References."]
[{"$nulls":4},"Optimization Under Uncertainty of Shock Control Bumps for Transonic Wings -- 1 Introduction -- 2 Gradient-Based Robust Design Framework -- 2.1 Motivation -- 2.2 Surrogate-Based Uncertainty Quantification -- 2.3 Obtaining the Gradients of the Statistics -- 2.4 Optimization Architecture -- 2.5 Application to Analytical Test Function -- 3 Application to the Robust Design of Shock Control Bumps: Problem Definition -- 3.1 Test Case -- 3.2 Numerical Model -- 3.3 Parametrization of Shock Control Bumps -- 3.4 Optimization Formulations -- 4 Results -- 4.1 Single-Point (Deterministic) Results -- 4.2 Uncertainty Quantification -- 4.3 Robust Results -- 5 Conclusions -- References -- Multi-Objective Design Optimisation of an Airfoil with Geometrical Uncertainties Leveraging Multi-Fidelity Gaussian Process Regression -- 1 Introduction -- 2 Design Optimisation Problem of Airfoil -- 3 Solvers -- 4 Multi-Fidelity Gaussian Process Regression -- 5 Uncertainty Treatment -- 6 Multi-Objective Optimisation Framework for Airfoil Optimisation Under Uncertainty -- 7 Results -- 8 Conclusion -- References -- High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted Optimisation -- 1 Introduction -- 2 Machine Learning Assisted Optimisation -- 2.1 Surrogate Model -- 2.2 Classifier -- 3 Quadrature Approach for Uncertainty Quantification -- 4 Problem Formulation -- 4.1 Optimisation Design Variables -- 4.2 High-Lift Devices Robust Optimisation Problem -- Original Objective Function -- Artificial Objective Function -- 5 Optimisation Setup -- 6 Results -- 7 Conclusions and Future Work -- References -- Network "]
[{"$nulls":4},"Resilience Optimisation of Complex Systems -- 1 Introduction -- 2 Evidence Theory as Uncertainty Framework -- 3 System Network Model -- 4 Complexity Reduction of Uncertainty Quantification -- 4.1 Network Decomposition -- 4.2 Tree-Based Exploration."]
[{"$nulls":4},"4.3 Combined Method."]