LEADER 02062 am 22003853u 450 001 9910306589203321 005 20200115 010 $a3-631-76154-6 024 7 $a10.3726/b14379 035 $a(CKB)4100000007523269 035 $a(OAPEN)1003355 035 $a(EXLCZ)994100000007523269 100 $a20200115d|||| uy 101 0 $ager 135 $auuuuu---auuuu 200 10$aUnsicherheit als Herausforderung fuer die Wissenschaft 210 $aBern$cPeter Lang International Academic Publishers$d2018 215 $a1 online resource (258) 311 $a3-631-76153-8 330 $aDas Buch präsentiert eine disziplinäre Vielfalt an Perspektiven auf Unsicherheit in der Wissenschaft. Schwerpunkte sind Klimaforschung, Umweltwissenschaft und Technikfolgenabschätzung. Die Beiträge diskutieren Gründe und Folgen wissenschaftlicher Unsicherheit und einer entsprechenden Verantwortung der Wissenschaft. Vertreten sind Kommunikationswissenschaft, Linguistik, Philosophie, Politikwissenschaft, Soziologie und Volkswirtschaftslehre sowie Chemie und Klimawissenschaft. Der Band dokumentiert die ungewöhnliche Kooperation zweier Schwerpunktprogramme der Deutschen Forschungsgemeinschaft ? «Wissenschaft und Öffentlichkeit» und «Climate Engineering: Risks, Challenges, Opportunities?» ?, die sich auf einer Tagung an der TU Darmstadt mit weiteren WissenschaftlerInnen zu Austausch und kritischer Reflexion getroffen haben. 517 $aWissen ? Kompetenz ? Text vol. 13 606 $aSemantics, discourse analysis, etc$2bicssc 606 $aInformation theory$2bicssc 606 $aRisk assessment$2bicssc 606 $aResearch methods: general$2bicssc 606 $aApplied ecology$2bicssc 615 7$aSemantics, discourse analysis, etc 615 7$aInformation theory 615 7$aRisk assessment 615 7$aResearch methods: general 615 7$aApplied ecology 906 $aBOOK 912 $a9910306589203321 996 $aUnsicherheit als Herausforderung fuer die Wissenschaft$91996914 997 $aUNINA LEADER 06328nam 22006735 450 001 9910254082303321 005 20251230065204.0 010 $a3-319-27517-8 024 7 $a10.1007/978-3-319-27517-8 035 $a(CKB)3710000000596695 035 $a(EBL)4406098 035 $a(SSID)ssj0001653861 035 $a(PQKBManifestationID)16433857 035 $a(PQKBTitleCode)TC0001653861 035 $a(PQKBWorkID)14982179 035 $a(PQKB)11157747 035 $a(DE-He213)978-3-319-27517-8 035 $a(MiAaPQ)EBC4406098 035 $a(PPN)192220357 035 $a(EXLCZ)993710000000596695 100 $a20160212d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSimulation-Driven Modeling and Optimization $eASDOM, Reykjavik, August 2014 /$fedited by Slawomir Koziel, Leifur Leifsson, Xin-She Yang 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (405 p.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v153 300 $aDescription based upon print version of record. 311 08$a3-319-27515-1 320 $aIncludes bibliographical references and index. 327 $aNumerical Aspects of Model Order Reduction for Gas Transportation Networks (Grundel, S., Hornung, N. & Roggendorf, S.) -- Parameter Studies for Energy Networks with Examples from Gas Transport (Clees, T.) -- Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation (Leifsson, L. et al.) -- Assessment of Inverse and Direct Methods for Airfoil and Wing Design (Zhang, M. & Rizzi, A.W.) -- Performance Optimization of EBG-Based Common Mode Filters for Signal Integrity Applications (Orlandi, A. et al.) -- Unattended Design of Wide-Band Planar Filters using a Two-Step Aggressive Space Mapping (ASM) Optimization Algorithm (Boria, V. et al.) -- Two-Stage Gaussian Process Modeling of Microwave Structures for Design Optimization (Jacobs, J.P. & Koziel, S.) -- Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks (Simsek, M. & Aoad, A.) -- Expedited Simulation-DrivenMulti-Objective Design Optimization of Quasi-Isotropic Dielectric Resonator Antenna (Bekasiewicz, A. et al.) -- Optimal Design of Photonic Crystal Nanostructures (Hassan, A.-K., Rafat, N. & Mohamed, A.S.A.) -- Design Optimization of LNAs and Reflectarray Antennas using the Full-Wave Simulation based Artificial Intelligence Models with the Novel Metaheuristic Algorithms (Güne?, F., Demirel, S. & Nesil, S.) -- Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives (Yeomans, J.S., Imanirad, R. & Yang, X.-S.) -- Linear and Nonlinear System Identification using Evolutionary Optimisation (Worden, K. et al.) -- A Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems with Inequality Constraints (Liu, B., Zhang, Q. & Gielen, G.) -- Sobol Indices for Dimension Adaptivity in Sparse Grids (Dwight, R.P., Desmedt, S.G.L., & Omrani, P.S.). . 330 $aThis edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth alongwith an array of the latest techniques to optimize the efficiency of the simulation-driven design process. High-fidelity simulation models allow for accurate evaluations of the devices and systems, which is critical in the design process, especially to avoid costly prototyping stages. Despite this and other advantages, the use of simulation tools in the design process is quite challenging due to associated high computational cost. The steady increase of available computational resources does not always translate into the shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. For this reason, automated simulation-driven design?while highly desirable?is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v153 606 $aMathematical optimization 606 $aCalculus of variations 606 $aMathematical models 606 $aMathematics$xData processing 606 $aCalculus of Variations and Optimization 606 $aMathematical Modeling and Industrial Mathematics 606 $aComputational Science and Engineering 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aMathematical models. 615 0$aMathematics$xData processing. 615 14$aCalculus of Variations and Optimization. 615 24$aMathematical Modeling and Industrial Mathematics. 615 24$aComputational Science and Engineering. 676 $a510 702 $aKoziel$b Slawomir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLeifsson$b Leifur$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYang$b Xin-She$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910254082303321 996 $aSimulation-driven modeling and optimization$91523626 997 $aUNINA