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Статті в журналах з теми "Multi-fidelity models"
Razi, Mani, Robert M. Kirby, and Akil Narayan. "Fast predictive multi-fidelity prediction with models of quantized fidelity levels." Journal of Computational Physics 376 (January 2019): 992–1008. http://dx.doi.org/10.1016/j.jcp.2018.10.025.
Повний текст джерелаPerdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis. "Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2198 (February 2017): 20160751. http://dx.doi.org/10.1098/rspa.2016.0751.
Повний текст джерелаRumpfkeil, Markus P., Dean Bryson, and Phil Beran. "Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems." Algorithms 15, no. 3 (March 21, 2022): 101. http://dx.doi.org/10.3390/a15030101.
Повний текст джерелаDiazDelaO, F. A., and S. Adhikari. "Bayesian assimilation of multi-fidelity finite element models." Computers & Structures 92-93 (February 2012): 206–15. http://dx.doi.org/10.1016/j.compstruc.2011.11.002.
Повний текст джерелаRumpfkeil, Markus P., and Philip Beran. "Multi-fidelity surrogate models for flutter database generation." Computers & Fluids 197 (January 2020): 104372. http://dx.doi.org/10.1016/j.compfluid.2019.104372.
Повний текст джерелаBonomo, Anthony L. "Multi-fidelity surrogate modeling for structural acoustics applications." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A287. http://dx.doi.org/10.1121/10.0018869.
Повний текст джерелаPeart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models." Journal of Sailing Technology 7, no. 01 (January 5, 2022): 31–51. http://dx.doi.org/10.5957/jst/2022.7.2.31.
Повний текст джерелаPeart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Multi-Fidelity Surrogate Models for VPP Aerodynamic Input Data." Journal of Sailing Technology 6, no. 01 (February 9, 2021): 21–43. http://dx.doi.org/10.5957/jst/2021.6.1.21.
Повний текст джерелаFarcaș, Ionuț-Gabriel, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, and Hans-Joachim Bungartz. "Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification." Computer Methods in Applied Mechanics and Engineering 406 (March 2023): 115908. http://dx.doi.org/10.1016/j.cma.2023.115908.
Повний текст джерелаStyler, Breelyn, and Reid Simmons. "Plan-Time Multi-Model Switching for Motion Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 558–66. http://dx.doi.org/10.1609/icaps.v27i1.13858.
Повний текст джерелаДисертації з теми "Multi-fidelity models"
Benamara, Tariq. "Full-field multi-fidelity surrogate models for optimal design of turbomachines." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2368.
Повний текст джерелаOptimizing turbomachinery components stands as a real challenge despite recent advances in theoretical, experimental and High-Performance Computing (HPC) domains. This thesis introduces and validates optimization techniques assisted by full-field Multi-Fidelity Surrogate Models (MFSMs) based on Proper Orthogonal Decomposition (POD). The combination of POD and Multi-Fidelity Modeling (MFM) techniques allows to capture the evolution of dominant flow features with geometry modifications. Two POD based multi-fidelity optimization methods are proposed. Thefirst one consists in an enrichment strategy dedicated to Gappy-POD (GPOD)models. It is more suitable for instantaneous low-fidelity computations whichmakes it hardly tractable for aerodynamic design of turbomachines. This methodis demonstrated on the flight domain study of a 2D airfoil from the literature. The second methodology is based on a multi-fidelity extension to Non-IntrusivePOD (NIPOD) models. This extension starts with a re-interpretation of theConstrained POD (CPOD) concept and allows to enrich the reduced spacedefinition with abondant, albeit inaccurate, low-fidelity information. In the second part of the thesis, a benchmark test case is introduced to test fullfield multi-fidelity optimization methodologies on an example presenting featuresrepresentative of turbomachinery problems. The predictability of the proposedMulti-Fidelity NIPOD (MFNIPOD) surrogate models is compared to classical surrogates from the literature on both analytical and industrial-scale applications. Finally, we employ the proposed tool to the shape optimization of a 1.5-stage boosterand we compare the obtained results with standard state of the art approaches
Belben, Joel Brian. "ENABLING RAPID CONCEPTUAL DESIGN USING GEOMETRY- BASED MULTI-FIDELITY MODELS IN VSP." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/969.
Повний текст джерелаHebert, James L. "Use of Multi-Fidelity and Surrogate Models to Reduce the Cost of Developing Physics-Based Systems." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687685.
Повний текст джерелаBuilding complex physics-based systems in a timely cost-effective manner, that perform well, meet diverse user needs, and have no bad emergent behaviors is a challenge. To meet these requirements the solution is to model the physics-based system before building it. Modeling and Simulation capabilities for these type systems have advanced continuously during the past 20 years thanks to progress in the application of high fidelity computational codes that are able to model the real physical performance of system components. The problem is that it is often too time consuming and costly to model complex systems, end-to-end, using these high fidelity computational models alone. Missing are good approaches to segment the modeling of complex systems performance and behaviors, keep the model chain coherent and only model what is necessary. Current research efforts have shown that using multi-fidelity and/or surrogate models might offer alternative methods of performing the modeling and simulations needed to design and develop physics-based systems more efficiently. This study demonstrates that it is possible reduce the number of high fidelity runs allowing the use of classical systems engineering analysis and tools that would not be possible if only high fidelity codes were employed. This study advances the systems engineering of physics-based systems by reducing the number of time consuming high fidelity models and simulations that must be used to design and develop the systems. The study produced a novel approach to the design and development of complex physics-based systems by using a mix of variable fidelity physics-based models and surrogate models. It shows that this combination of increasing fidelity models enables the computationally and cost efficient modeling and simulation of these complex systems and their components. The study presents an example of the methodology for the analysis and design of two physics-based systems: a Ground Penetrating Radar (GPR) and a Nuclear Electromagnetic Pulse Bounded Wave System.
Raub, Corey Bevan. "Geometric analysis of axisymmetric disk forging." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172778393.
Повний текст джерелаAulmann, Maria. "Entwicklung und Evaluierung von Clinical Skills - Simulatoren für die Lehre in der Tiermedizin." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-215224.
Повний текст джерелаIntroduction Students of veterinary medicine are expected to acquire various practical skills in addition to a wide range of theoretical knowledge. There is a strong demand for training opportunities, as every individual learns and acquires practical skills at individual pace. For reasons of animal welfare concerns and availability, live animals and cadavers cannot always be used for clinical skills training. Simulation models, which are models of organisms or body parts can be a considerable alternative for clinical skills training. Models that are commercially produced often have a high price and are not available for all skills. Self-made models are increasingly used in veterinary education. Because there is few published data regarding their use, more scientific research is required. Aims of the Investigation The objective of this study was to determine, if self-made low-fidelity models can be successfully used in veterinary medical education. For this purpose, two self-made low-fidelity models were evaluated (study 1) and their use in combination with other teaching tools was analyzed (study 2). Materials and Methods In study 1, two self-made low-fidelity models for simulation of canine intubation and canine female urinary catheterization were developed and evaluated. We used a study design that compares acquired skills of two intervention groups and one control group in a practical examination (OSCE = objective structured clinical examination). Fifty-eight second-year veterinary medicine students received a theoretical introduction to intubation and were randomly divided into three groups. Group 1 (high-fidelity) was then trained on a commercially available Intubation Training Manikin, group 2 (low-fidelity) was trained on our low-fidelity model, and the text group read a text describing intubation of the dog. Forty-seven fifth-year veterinary medicine students followed the same procedure for training urinary catheterization using the commercially available Female Urinary Catheter Training Manikin, our self-made model, and text. Outcomes were assessed in a practical examination on a cadaver using an OSCE checklist. In study 2 we evaluated the teaching of two specific clinical skills using potcasts and low-fidelity simulation training. Two instructional potcasts describing intubation and catheterization and both low-fidelity models described above were used. In our study, potcasts are audio-visual animations that provide the learner with step by step information and instruction on a clinical skill. We used a crossover study design and compared the acquired practical skills of two intervention groups after a different theoretical preparation. A survey captured the participants’ feedback. Sixty first year veterinary medicine students were randomly allocated to two groups, a potcast group and a text group. The potcast group watched a potcast while the text group read an instructional text for preparation. Then both groups had separate self-directed training sessions on low-fidelity models. Outcomes were assessed in practical examinations on a cadaver using an objective structured clinical examination (OSCE) checklist. Results In study 1 all intervention groups performed significantly better than the text groups. Group I (high-fidelity) and group II (low-fidelity) for both intubation and catheterization showed no significant differences. In study 2 the potcast group performed significantly better than the text group in study intubation but no significant differences were observed in study catheterization. Overall, participants enjoyed clinical skills training but experienced self-directed learning as challenging. Conclusion Low-fidelity models can be as effective as high-fidelity models for clinical skills training. Clinical skills training using potcasts and self-directed low-fidelity simulation training should be complemented by supervisor or peer instruction rather than used as exclusive tool for teaching first year veterinary students. We assume though, that self-directed learning instructed by our potcasts can be a valuable chance for deepening and repetitive training of higher semesters. The use of simulation models in veterinary education has been consistently increasing in the past few years. This study is an important, timely contribution to the evaluation of simulation based education
Biehler, Jonas [Verfasser], Wolfgang A. [Akademischer Betreuer] [Gutachter] Wall, and Phaedon-Stelios [Gutachter] Koutsourelakis. "Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach / Jonas Biehler ; Gutachter: Wolfgang A. Wall, Phaedon-Stelios Koutsourelakis ; Betreuer: Wolfgang A. Wall." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1123729220/34.
Повний текст джерелаSacher, Matthieu. "Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition." Thesis, Paris, ENSAM, 2018. http://www.theses.fr/2018ENAM0032/document.
Повний текст джерелаSailing yacht performance optimization is a difficult problem due to the high complexity of the mechanicalsystem (aero-elastic and hydrodynamic coupling) and the large number of parameters to optimize (sails, rigs, etc.).Despite the fact that sailboats optimization is empirical in most cases today, the numerical optimization approach is nowconsidered as possible because of the latest advances in physical models and computing power. However, these numericaloptimizations remain very expensive as each simulation usually requires solving a non-linear fluid-structure interactionproblem. Thus, the central objective of this thesis is to propose and to develop original methods aiming at minimizing thenumerical cost of sailing yacht performance optimization. The Efficient Global Optimization (EGO) is therefore appliedto solve various optimization problems. The original EGO method is extended to cases of optimization under constraints,including possible non computable points, using a classification-based approach. The use of multi-fidelity surrogates isalso adapted to the EGO method. The applications treated in this thesis concern the original optimization problems inwhich the performance is modeled experimentally and/or numerically. These various applications allow for the validationof the developments in optimization methods on real and complex problems, including fluid-structure interactionphenomena
Chetry, Manisha. "Advanced reduced-order modeling and parametric sampling for non-Newtonian fluid flows." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0011.
Повний текст джерелаThe subject of this thesis concernsmodel-order reduction (MOR) of parameterizednon-Newtonian flow problems that havesignificant industrial applications. TraditionalMOR methods constrain the computationalperformance of such highly nonlinear problems,so we suggest a state-of-the-art hyper-reductiontechnique based on a sparse approximation totackle the evaluation of nonlinear terms at muchreduced complexity. We also provide offlinestabilization strategy for stabilizing theconstitutive model in the reduced order modelframework that is less expensive to computewhile maintaining the full order model's (FOM)accuracy. Combining the two significantlylowers the CPU cost as compared to the FOMevaluation which inevitably boosts MORperformance. This work is validated on twobenchmark flow problems. Additionally, anadaptive sampling strategy is also presented inthis manuscript which is achieved byleveraging multi-fidelity model approximation.Towards the end of the thesis, we addressanother issue that is typically observed forcases when adaptive finite element meshesare deployed. In such cases, MOR methods failto produce a low-dimensional representationsince the snapshots are not vectors of samelength. We therefore, suggest an alternatemethod that can generate reduced basisfunctions for database of space-adaptedsnapshots
Eaton, Ammon Nephi. "Multi-Fidelity Model Predictive Control of Upstream Energy Production Processes." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6376.
Повний текст джерелаMAININI, LAURA. "Multidisciplinary and multi-fidelity optimization environment for wing integrated design." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2500000.
Повний текст джерелаЧастини книг з теми "Multi-fidelity models"
Jiang, Ping, Qi Zhou, and Xinyu Shao. "Multi-fidelity Surrogate Models." In Surrogate Model-Based Engineering Design and Optimization, 55–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0731-1_4.
Повний текст джерелаNachar, S., P. A. Boucard, D. Néron, U. Nackenhorst, and A. Fau. "Multi-fidelity Metamodels Nourished by Reduced Order Models." In Virtual Design and Validation, 61–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38156-1_4.
Повний текст джерелаKoziel, Slawomir, and Leifur Leifsson. "Multi-objective Optimization Using Variable-Fidelity Models and Response Correction." In Simulation-Driven Design by Knowledge-Based Response Correction Techniques, 193–210. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30115-0_11.
Повний текст джерелаPoethke, Bernhard, Stefan Völker, and Konrad Vogeler. "Aerodynamic Optimization of Turbine Airfoils Using Multi-fidelity Surrogate Models." In EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization, 556–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97773-7_50.
Повний текст джерелаPeri, Daniele, Antonio Pinto, and Emilio F. Campana. "Multi-Objective Optimisation of Expensive Objective Functions with Variable Fidelity Models." In Nonconvex Optimization and Its Applications, 223–41. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30065-1_14.
Повний текст джерелаLeifsson, Leifur, Slawomir Koziel, Yonatan Tesfahunegn, and Adrian Bekasiewicz. "Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation." In Simulation-Driven Modeling and Optimization, 55–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27517-8_3.
Повний текст джерелаFan, Yiming, and Fotis Kopsaftopoulos. "Damage State Estimation via Multi-fidelity Gaussian Process Regression Models for Active-Sensing Structure Health Monitoring." In Lecture Notes in Civil Engineering, 267–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07258-1_28.
Повний текст джерелаSchürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.
Повний текст джерелаSchürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.
Повний текст джерелаRosenberg, Jonathan, Mark Sherman, Ann Marks, and Jaap Akkerhuis. "Document Models and Interchange Fidelity." In Multi-media Document Translation, 21–36. New York, NY: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4684-6404-7_2.
Повний текст джерелаТези доповідей конференцій з теми "Multi-fidelity models"
Cambeiro, Joao, Julien Deantoni, and Vasco Amaral. "Supporting the Engineering of Multi-Fidelity Simulation Units With Simulation Goals." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00053.
Повний текст джерелаThenon, A., V. Gervais, and M. Le Ravalec. "Multi-fidelity Proxy Models for Reservoir Engineering." In ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2016. http://dx.doi.org/10.3997/2214-4609.201601831.
Повний текст джерелаFrigerio, Nicla, Andrea Matta, and Ziwei Lin. "MULTI-FIDELITY MODELS FOR DECOMPOSED SIMULATION OPTIMIZATION PROBLEMS." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632480.
Повний текст джерелаNelson, Andrea, Juan Alonso, and Thomas Pulliam. "Multi-Fidelity Aerodynamic Optimization Using Treed Meta-Models." In 25th AIAA Applied Aerodynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-4057.
Повний текст джерелаPhelivan Soak, H., J. Wackers, R. Pellegrini, A. Serani, M. Diez, R. Perali, M. Sacher, et al. "Hydrofoil Optimization via Automated Multi-Fidelity Surrogate Models." In 10th Conference on Computational Methods in Marine Engineering. CIMNE, 2023. http://dx.doi.org/10.23967/marine.2023.136.
Повний текст джерелаPai, Sai G. S., and Ian F. C. Smith. "Multi-fidelity modelling for structural identification." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1092.
Повний текст джерелаPang, Bowen, Xiaolei Xie, Betnd Heidergott, and Yijie Peng. "optimizing outpatient Department Staffing Level using Multi-Fidelity Models." In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE, 2019. http://dx.doi.org/10.1109/coase.2019.8842984.
Повний текст джерелаJaeggi, Daniel, Geoff Parks, William Dawes, and John Clarkson. "Robust Multi-Fidelity Aerodynamic Design Optimization Using Surrogate Models." In 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.
Повний текст джерелаSendrea, Ricardo E., Constantinos L. Zekios, and Stavros V. Georgakopoulos. "A Multi-Fidelity Surrogate Optimization Method Based on Analytical Models." In 2021 IEEE/MTT-S International Microwave Symposium - IMS 2021. IEEE, 2021. http://dx.doi.org/10.1109/ims19712.2021.9574986.
Повний текст джерелаYong, Hau Kit, Leran Wang, David J. J. Toal, Andy J. Keane, and Felix Stanley. "Multi-Fidelity Kriging-Based Optimization of Engine Subsystem Models With Medial Meshes." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76148.
Повний текст джерелаЗвіти організацій з теми "Multi-fidelity models"
Balachandran, B. Leveraging Multi-Fidelity Models for Flexible Wing Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada611076.
Повний текст джерелаMaulik, Romit, Virendra Ghate, William Pringle, Yan Feng, Vishwas Rao, Julie Bessac, and Bethany Lusch. Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769781.
Повний текст джерелаYin, Lin. IC Report for project “Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED”. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1846881.
Повний текст джерелаYin, Lin. IC Report for project “Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED”. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1968190.
Повний текст джерелаModest, Michael. AOI 1— COMPUTATIONAL ENERGY SCIENCES:MULTIPHASE FLOW RESEARCH High-fidelity multi-phase radiation module for modern coal combustion systems. Office of Scientific and Technical Information (OSTI), November 2013. http://dx.doi.org/10.2172/1134746.
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