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001441865 001__ 1441865
001441865 003__ OCoLC
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001441865 019__ $$a1294219495$$a1294283527$$a1295276756$$a1296430507
001441865 020__ $$a9783030805425$$q(electronic bk.)
001441865 020__ $$a3030805425$$q(electronic bk.)
001441865 020__ $$z9783030805418
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001441865 0247_ $$a10.1007/978-3-030-80542-5$$2doi
001441865 035__ $$aSP(OCoLC)1296685798
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001441865 049__ $$aISEA
001441865 050_4 $$aTL560$$b.I58 2020
001441865 08204 $$a629.101519544$$223
001441865 1112_ $$aInternational Conference on Uncertainty Quantification & Optimisation$$d(2020 :$$cOnline)
001441865 24510 $$aAdvances in uncertainty quantification and optimization under uncertainty with aerospace applications :$$bproceedings of the 2020 UQOP International Conference /$$cMassimiliano Vasile, Domenico Quagliarella, editors.
001441865 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001441865 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and color).
001441865 336__ $$atext$$btxt$$2rdacontent
001441865 337__ $$acomputer$$bc$$2rdamedia
001441865 338__ $$aonline resource$$bcr$$2rdacarrier
001441865 4901_ $$aSpace technology proceedings ;$$vvolume 8
001441865 500__ $$aIncludes index.
001441865 5050_ $$aChapter 1. Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems -- Chapter 2. Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty using Far-Field Drag Approximation -- Chapter 3. Scalable dynamic asynchronous Monte Carlo framework applied to wind engineering problems -- Chapter 4. Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE -- Chapter 5. From Uncertainty Quanti cation to Shape Optimization: Cross-Fertilization of Methods for Dimensionality Reduction -- Chapter 6. Multi-Objective Robustness Analysis of the Polymer Extrusion Process -- Chapter 7. Quantification of operational and geometrical uncertainties of a 1.5 stage axial compressor with cavity leakage flows -- Chapter 8. Can Uncertainty Propagation Solve the Mysterious Case of Snoopy ? -- Chapter 9. Robust Particle Filter for Space Navigation under Epistemic Uncertainty -- Chapter 10. Computing bounds for imprecise continuous-time Markov chains using normal cones -- Chapter 11. Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference -- Chapter 12. Computing Expected Hitting Times for Imprecise Markov Chains -- Chapter 13. Multi-Objective Robust Trajectory Optimization of Multi Asteroid Fly-By Under Epistemic Uncertainty -- Chapter 14. Reliability-based Robust Design Optimization of a Jet Engine Nacelle -- Chapter 15. Bayesian Optimization for Robust Solutions under Uncertain Input -- Chapter 16. Optimization under Uncertainty of Shock Control Bumps for Transonic Wings -- Chapter 17. Multi-objective design optimisation of an airfoil with geometrical uncertainties leveraging multi- delity Gaussian process regression -- Chapter 18. High-Lift Devices Topology Robust Optimisation using Machine Learning Assisted Optimisation -- Chapter 19. Network Resilience Optimisation of Complex Systems -- Chapter 20. Gaussian Processes for CVaR approximation in Robust Aerodynamic Shape Design -- Chapter 21. Inference methods for gas/surface interaction models: from deterministic approaches to Bayesian techniques -- Chapter 22. Bayesian Adaptive Selection Under Prior Ignorance -- Chapter 23. A Machine-Learning Framework for Plasma-Assisted Combustion using Principal Component Analysis and Gaussian Process Regression -- Chapter 24. Estimating exposure fraction from radiation biomarkers: a comparison of frequentist and Bayesian approaches -- Chapter 25. A Review of some recent advancements in Non-Ideal Compressible Fluid Dynamics -- Chapter 26. Dealing with high dimensional inconsistent measurements in inverse problems using surrogate modeling: an approach based on sets and intervals -- Chapter 27. Stochastic Preconditioners for Domain Decomposition Methods -- Index.
001441865 506__ $$aAccess limited to authorized users.
001441865 520__ $$aThe 2020 International Conference on Uncertainty Quantification & Optimization gathered together internationally renowned researchers in the fields of optimization and uncertainty quantification. The resulting proceedings cover all related aspects of computational uncertainty management and optimization, with particular emphasis on aerospace engineering problems. The book contributions are organized under four major themes: Applications of Uncertainty in Aerospace & Engineering Imprecise Probability, Theory and Applications Robust and Reliability-Based Design Optimisation in Aerospace Engineering Uncertainty Quantification, Identification and Calibration in Aerospace Models This proceedings volume is useful across disciplines, as it brings the expertise of theoretical and application researchers together in a unified framework.
001441865 588__ $$aDescription based on print version record.
001441865 650_0 $$aAeronautics$$xStatistical methods$$vCongresses.
001441865 650_0 $$aMeasurement uncertainty (Statistics)$$vCongresses.
001441865 650_0 $$aMathematical optimization$$vCongresses.
001441865 650_6 $$aAéronautique$$xMéthodes statistiques$$vCongrès.
001441865 650_6 $$aIncertitude de mesure$$vCongrès.
001441865 650_6 $$aOptimisation mathématique$$vCongrès.
001441865 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001441865 655_7 $$aConference papers and proceedings.$$2lcgft
001441865 655_7 $$aActes de congrès.$$2rvmgf
001441865 655_0 $$aElectronic books.
001441865 7001_ $$aVasile, Massimiliano,$$eeditor.
001441865 7001_ $$aQuagliarella, D.,$$eeditor.
001441865 77608 $$iPrint version:$$aInternational Conference on Uncertainty Quantification and Optimization (2020), creator.$$tAdvances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications.$$dCham : Springer, 2021$$z9783030805418$$w(OCoLC)1264402663
001441865 830_0 $$aSpace technology proceedings ;$$vv. 8.
001441865 852__ $$bebk
001441865 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-80542-5$$zOnline Access$$91397441.1
001441865 909CO $$ooai:library.usi.edu:1441865$$pGLOBAL_SET
001441865 980__ $$aBIB
001441865 980__ $$aEBOOK
001441865 982__ $$aEbook
001441865 983__ $$aOnline
001441865 994__ $$a92$$bISE