Littérature scientifique sur le sujet « Multi-fidelity Analysi »
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Articles de revues sur le sujet "Multi-fidelity Analysi"
Lee, Daeyeon, Nhu Van Nguyen, Maxim Tyan, Hyung-Geun Chun, Sangho Kim et Jae-Woo Lee. « Enhanced multi-fidelity model for flight simulation using global exploration and the Kriging method ». Proceedings of the Institution of Mechanical Engineers, Part G : Journal of Aerospace Engineering 231, no 4 (6 août 2016) : 606–20. http://dx.doi.org/10.1177/0954410016641441.
Texte intégralRumpfkeil, Markus P., Dean Bryson et Phil Beran. « Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems ». Algorithms 15, no 3 (21 mars 2022) : 101. http://dx.doi.org/10.3390/a15030101.
Texte intégralForrester, Alexander I. J., András Sóbester et Andy J. Keane. « Multi-fidelity optimization via surrogate modelling ». Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences 463, no 2088 (2 octobre 2007) : 3251–69. http://dx.doi.org/10.1098/rspa.2007.1900.
Texte intégralBonfiglio, Luca, Paris Perdikaris et Stefano Brizzolara. « Multi-fidelity Bayesian Optimization of SWATH Hull Forms ». Journal of Ship Research 64, no 02 (1 juin 2020) : 154–70. http://dx.doi.org/10.5957/jsr.2020.64.2.154.
Texte intégralThelen, Andrew S., Dean E. Bryson, Bret K. Stanford et Philip S. Beran. « Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations ». Algorithms 15, no 4 (16 avril 2022) : 131. http://dx.doi.org/10.3390/a15040131.
Texte intégralWei, Yunfei, et Shifeng Xiong. « Bayesian integrative analysis for multi-fidelity computer experiments ». Journal of Applied Statistics 46, no 11 (4 février 2019) : 1973–87. http://dx.doi.org/10.1080/02664763.2019.1575340.
Texte intégralKlimczyk, Witold Artur, et Zdobyslaw Jan Goraj. « Analysis and optimization of morphing wing aerodynamics ». Aircraft Engineering and Aerospace Technology 91, no 3 (4 mars 2019) : 538–46. http://dx.doi.org/10.1108/aeat-12-2017-0289.
Texte intégralFu, Wenbo, Qiushi Li, Yongshun Song, Yaogen Shu, Zhongcan Ouyang et Ming Li. « Theoretical analysis of RNA polymerase fidelity : a steady-state copolymerization approach ». Communications in Theoretical Physics 74, no 1 (10 décembre 2021) : 015601. http://dx.doi.org/10.1088/1572-9494/ac3993.
Texte intégralKandasamy, Kirthevasan, Gautam Dasarathy, Junier Oliva, Jeff Schneider et Barnabás Póczos. « Multi-fidelity Gaussian Process Bandit Optimisation ». Journal of Artificial Intelligence Research 66 (15 septembre 2019) : 151–96. http://dx.doi.org/10.1613/jair.1.11288.
Texte intégralYounis, Adel, et Zuomin Dong. « High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm ». Algorithms 15, no 8 (7 août 2022) : 279. http://dx.doi.org/10.3390/a15080279.
Texte intégralThèses sur le sujet "Multi-fidelity Analysi"
MAININI, LAURA. « Multidisciplinary and multi-fidelity optimization environment for wing integrated design ». Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2500000.
Texte intégralLe, Gratiet Loic. « Multi-fidelity Gaussian process regression for computer experiments ». Phd thesis, Université Paris-Diderot - Paris VII, 2013. http://tel.archives-ouvertes.fr/tel-00866770.
Texte intégralMuppana, Sai. « Multi-fidelity Design and Analysis of Single Hub Multi-rotor High Pressure Centrifugal Compressor ». University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553517075653458.
Texte intégralVenkatesan, Sreedhar, et Hanumantha Raju Hariprasad Banglore. « Probabilistic Analysis of Brake Noise : A Hierarchical Multi-fidelity Statistical Approach ». Thesis, Linköpings universitet, Mekanik och hållfasthetslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151009.
Texte intégralComputer Aided Engineering (cae) driven analysis is gaining grounds in automotive industry. Brake noise is one such place where cae simulations are gaining more attention. The presence of several uncertain parameters which affect brake noises and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on cae deterministic analyses alone.Therefore, the confidence level in cae analyses has to be increased to ensure cae analysis robustness. One way to achieve this is by incorporating the effects of different sources of uncertainty and variability in the cae analysis and estimating the probability of design failure. Such a reliability measure (i.e. probability of noise event occurrence or exceedance of noise level than a threshold) can provide car manufacturers with an idea about the costs of warranty claims due to brake noise and can be used as a metric to evaluate different design solutions, before the final design goes to the production stage. On one hand, using the high-fidelity models of brake/chassis system is generally computationally intensive, and thus, often only limited number of simula-tion runs are feasible for uncertainty analysis and design failure risk assessment. On the other hand, analyses on low-fidelity models, typically based on simplified assumptions during the development phase are fast but not always accu-rate. Striking for a good balance between efficiency and accuracy/robustness is an important task, when dealing with uncertainty/risk analysis of such complex dynamical systems To address these issues, a hierarchical multi-fidelity statistical approach has been adopted in this study, in order to estimate the probability of design failure. It employs a hierarchy of approximations to the system response computed with different fidelity by surrogate modelling, coarse spatial/temporal model mesh resolution variation, changing solver time step, etc., using probability theory, to relate information provided by approximate solu-tions to the target failure estimation. Using this approach opens up the possi-bility to use a low-fidelity models to accelerate the uncertainty quantification of complex brake/chassis systems, while granting unbiased estimation of system design failure risk/reliability. It also enables management of design changes, during fast iterations of the design process. This approach is used for studying one of the brake noise issue called creep groan, understand the root cause and providedesign proposals.
Loupy, Gaëtan J. M. « High fidelity, multi-disciplinary analysis of flow in realistic weapon bays ». Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/9091/.
Texte intégralStults, Ian Collier. « A multi-fidelity analysis selection method using a constrained discrete optimization formulation ». Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31706.
Texte intégralCommittee Chair: Mavris, Dimitri; Committee Member: Beeson, Don; Committee Member: Duncan, Scott; Committee Member: German, Brian; Committee Member: Kumar, Viren. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Lawson, Stephen James. « High performance computing for high-fidelity multi-disciplinary analysis of weapon bays ». Thesis, University of Liverpool, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533992.
Texte intégralMeckstroth, Christopher. « Incorporation of Physics-Based Controllability Analysis in Aircraft Multi-Fidelity MADO Framework ». University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575557306181006.
Texte intégralRaj, Oliver Neal. « Multi-Fidelity Structural Modeling For Set Based Design of Advanced Marine Vehicles ». Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83377.
Texte intégralMaster of Science
Mola, Andrea. « Multi-physics and Multilevel Fidelity Modeling and Analysis of Olympic Rowing Boat Dynamics ». Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28057.
Texte intégralPh. D.
Chapitres de livres sur le sujet "Multi-fidelity Analysi"
Tejika, Shintaro, Takahiro Fujikawa et Koichi Yonemoto. « Multi-objective System Optimization of Suborbital Spaceplane by Multi-fidelity Aerodynamic Analysis ». Dans Lecture Notes in Electrical Engineering, 283–96. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2689-1_21.
Texte intégralElham, Ali, et Michel J. L. van Tooren. « Trust Region Filter-SQP Method for Multi-Fidelity Wing Aerostructural Optimization ». Dans Variational Analysis and Aerospace Engineering, 247–67. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45680-5_10.
Texte intégralTung, Y.-C., C.-T. Lin, K. Kurabayashi et S. J. Skerlos. « High Fidelity and Low Cost Detection of Multi-Color Fluorescence from Biological Cells in a Micro Integrated Flow Cytometer (MIFC) with Disposable Observation Cell ». Dans Micro Total Analysis Systems 2002, 254–56. Dordrecht : Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0295-0_85.
Texte intégralFitzgerald, John, Ken Pierce et Peter Gorm Larsen. « Collaborative Development of Dependable Cyber-Physical Systems by Co-Modeling and Co-Simulation ». Dans Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–28. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6194-3.ch001.
Texte intégralDay, Kirsten. « John Ford’s The Searchers1 ». Dans Cowboy Classics. Edinburgh University Press, 2016. http://dx.doi.org/10.3366/edinburgh/9781474402460.003.0006.
Texte intégralActes de conférences sur le sujet "Multi-fidelity Analysi"
Balabanov, Vladmir, et Gerhard Venter. « Multi-Fidelity Optimization with High-Fidelity Analysis and Low-Fidelity Gradients ». Dans 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4459.
Texte intégralFischer, Christopher C., et Ramana V. Grandhi. « Multi-Fidelity Design Optimization via Low-Fidelity Correction Technique ». Dans 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4293.
Texte intégralOlson, Erik D. « Multi-Disciplinary, Multi-Fidelity Discrete Data Transfer Using Degenerate Geometry Forms ». Dans 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3208.
Texte intégralJain, Samarth, William A. Crossley et Satadru Roy. « A Multi-Fidelity Approach to Address Multi-Objective Mixed-Discrete Nonlinear Programming Problems ». Dans 2018 Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-3414.
Texte intégralJaeggi, Daniel, Geoff Parks, William Dawes et John Clarkson. « Robust Multi-Fidelity Aerodynamic Design Optimization Using Surrogate Models ». Dans 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6052.
Texte intégralAlonso, Juan, Patrick LeGresley, Edwin Van der Weide, Joaquim R. R. A. Martins et James Reuther. « pyMDO : A Framework for High-Fidelity Multi-Disciplinary Optimization ». Dans 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4480.
Texte intégralHaimes, Robert, John Dannenhoffer, Nitin D. Bhagat et Darcy L. Allison. « Multi-fidelity Geometry-centric Multi-disciplinary Analysis for Design ». Dans AIAA Modeling and Simulation Technologies Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4007.
Texte intégralNigam, Nikhil, Ankit Tyagi, Peter Chen, Juan J. Alonso, Francisco Palacios, Michael V. Ol et John Byrnes. « Multi-Fidelity Multi-Disciplinary Propeller/Rotor Analysis and Design ». Dans 53rd AIAA Aerospace Sciences Meeting. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0029.
Texte intégralGhoreishi, Seyede Fatemeh, et Douglas L. Allaire. « Gaussian Process Regression for Bayesian Fusion of Multi-Fidelity Information Sources ». Dans 2018 Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-4176.
Texte intégralMacDonald, Timothy, Matthew Clarke, Emilio M. Botero, Julius M. Vegh et Juan J. Alonso. « SUAVE : An Open-Source Environment Enabling Multi-Fidelity Vehicle Optimization ». Dans 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-4437.
Texte intégralRapports d'organisations sur le sujet "Multi-fidelity Analysi"
Ayoul-Guilmard, Q., S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi et C. Soriano. D5.4 Report on MLMC for time dependent problems. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.005.
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