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Статті в журналах з теми "Multifidelity models":
Molléro, Roch, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, and Maxime Sermesant. "Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models." Biomechanics and Modeling in Mechanobiology 17, no. 1 (September 11, 2017): 285–300. http://dx.doi.org/10.1007/s10237-017-0960-0.
Jacobs, Ryan, Philip E. Goins, and Dane Morgan. "Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap." Machine Learning: Science and Technology 4, no. 4 (December 1, 2023): 045060. http://dx.doi.org/10.1088/2632-2153/ad1627.
Narayan, Akil, Claude Gittelson, and Dongbin Xiu. "A Stochastic Collocation Algorithm with Multifidelity Models." SIAM Journal on Scientific Computing 36, no. 2 (January 2014): A495—A521. http://dx.doi.org/10.1137/130929461.
Peng, Yijie, Jie Xu, Loo Hay Lee, Jianqiang Hu, and Chun-Hung Chen. "Efficient Simulation Sampling Allocation Using Multifidelity Models." IEEE Transactions on Automatic Control 64, no. 8 (August 2019): 3156–69. http://dx.doi.org/10.1109/tac.2018.2886165.
Jasa, John, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter. "Effectively using multifidelity optimization for wind turbine design." Wind Energy Science 7, no. 3 (May 11, 2022): 991–1006. http://dx.doi.org/10.5194/wes-7-991-2022.
Rumpfkeil, Markus P., and Philip Beran. "Construction of Dynamic Multifidelity Locally Optimized Surrogate Models." AIAA Journal 55, no. 9 (September 2017): 3169–79. http://dx.doi.org/10.2514/1.j055834.
Zhu, Xueyu, Akil Narayan, and Dongbin Xiu. "Computational Aspects of Stochastic Collocation with Multifidelity Models." SIAM/ASA Journal on Uncertainty Quantification 2, no. 1 (January 2014): 444–63. http://dx.doi.org/10.1137/130949154.
Keshavarzzadeh, Vahid, Robert M. Kirby, and Akil Narayan. "Convergence Acceleration for Time-Dependent Parametric Multifidelity Models." SIAM Journal on Numerical Analysis 57, no. 3 (January 2019): 1344–68. http://dx.doi.org/10.1137/18m1170339.
Howard, Amanda, Yucheng Fu, and Panos Stinis. "A multifidelity approach to continual learning for physical systems." Machine Learning: Science and Technology 5, no. 2 (May 16, 2024): 025042. http://dx.doi.org/10.1088/2632-2153/ad45b2.
Pienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems." Biomedical Engineering and Computational Biology 9 (January 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.
Дисертації з теми "Multifidelity models":
Robinson, Theresa Dawn 1978. "Surrogate-based optimization using multifidelity models with variable parameterization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39666.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 131-138).
Engineers are increasingly using high-fidelity models for numerical optimization. However, the computational cost of these models, combined with the large number of objective function and constraint evaluations required by optimization methods, can render such optimization computationally intractable. Surrogate-based optimization (SBO) - optimization using a lower-fidelity model most of the time, with occasional recourse to the high-fidelity model - is a proven method for reducing the cost of optimization. One branch of SBO uses lower-fidelity physics models of the same system as the surrogate. Until now however, surrogates using a different set of design variables from that of the high-fidelity model have not been available to use in a provably convergent numerical optimization. New methods are herein developed and demonstrated to reduce the computational cost of numerical optimization of variableparameterization problems, that is, problems for which the low-fidelity model uses a different set of design variables from the high-fidelity model.
(cont.) Four methods are presented to perform mapping between variable-parameterization spaces, the last three of which are new: space mapping, corrected space mapping, a mapping based on proper orthogonal decomposition (POD), and a hybrid between POD mapping and space mapping. These mapping methods provide links between different models of the same system and have further applications beyond formal optimization strategies. On an unconstrained airfoil design problem, it achieved up to 40% savings in highfidelity function evaluations. On a constrained wing design problem it achieved 76% time savings, and on a bat flight design problem, it achieved 45% time savings. On a large-scale practical aerospace application, such time savings could represent weeks.
by Theresa D. Robinson.
Ph.D.
Chandrasekhar, Ashok. "Interfacing geometric design models to analyzable product models with multifidelity and mismatched analysis geometry." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/17769.
Battisti, Beatrice. "Modélisation multi-échelle et multi-fidélité pour des extracteurs d'énergie marine." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0072.
The marine sector is increasingly turning to wave energy converters (WECs) for clean energy generation. For commercial-scale production, WEC farm deployment is essential, but requires complex numerical simulations. While high-fidelity models like Computational Fluid Dynamics (CFD) ensure accuracy, their substantial computational demands have prompt interest in model order reduction techniques. Proper Orthogonal Decomposition (POD) projection-based reduced order models have proven effective in monophase flows, yet face stability issues with multiphase flows. A proposed multi-fidelity model integrates CFD for WEC near-field description, and POD for far-field wave propagation. Bidirectional information exchange ensures precise flow reconstruction and floater dynamics description. Testing confirms its efficacy in various scenarios, significantly reducing the computational burden, decisive for tackling WEC farm design and optimization
Il settore marittimo è sempre più orientato verso i convertitori di energia delle onde (WECs), in particolare verso i parchi di WECs. Tuttavia, le simulazioni numeriche sono complesse e, sebbene i modelli ad alta fedeltà assicurino precisione, i loro requisiti computazionali hanno stimolato l’interesse per le tecniche di riduzione di modello. I modelli a ordine ridotto basati sulla Decomposizione Ortogonale ai valori Propri (POD) sono efficaci per flussi monofase, ma incontrano problemi di stabilità con i flussi multifase. Un modello multifideltà è proposto, che integra la CFD (Computational Fluid Dynamics) per il campo vicino ai WECs e la POD per la propagazione delle onde nel campo lontano. Lo scambio di informazioni assicura una descrizione precisa del flusso e della dinamica dei WECs. I test ne confermano l’efficacia, riducendo significativamente il carico computazionale, cruciale per affrontare l’ottimizzazione dei parchi di WECs
Fossà, Alberto. "Propagation multi-fidélité d’incertitude orbitale en présence d’accélérations stochastiques." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0009.
The problem of nonlinear uncertainty propagation (UP) is crucial in astrodynamics since all systems of practical interest, ranging from navigation to orbit determination (OD) and target tracking, involve nonlinearities in their dynamics and measurement models. One topic of interest is the accurate propagation of uncertainty through the nonlinear orbital dynamics, a fundamental requirement in several applications such as space surveillance and tracking (SST), space traffic management (STM), and end-of-life (EOL) disposal. Given a finite-dimensional representation of the probability density function (pdf) of the initial state, the main goal is to obtain a similar representation of the state pdf at any future time. This problem has been historically tackled with either linearized methods or Monte Carlo (MC) simulations, both of which are unsuitable to satisfy the demand of a rapidly growing number of applications. Linearized methods are light on computational resources, but cannot handle strong nonlinearities or long propagation windows due to the local validity of the linearization. In contrast, MC methods can handle any kind of nonlinearity, but are too computationally expensive for any task that requires the propagation of several pdfs. Instead, this thesis leverages multifidelity methods and differential algebra (DA) techniques to develop computationally efficient methods for the accurate propagation of uncertainties through nonlinear dynamical systems. The first method, named low-order automatic domain splitting (LOADS), represents the uncertainty with a set of second-order Taylor polynomials and leverages a DA-based measure of nonlinearity to adjust their number based on the local dynamics and the required accuracy. An adaptive Gaussian mixture model (GMM) method is then developed by associating each polynomial to a weighted Gaussian kernel, thus obtaining an analytical representation of the state pdf. Going further, a multifidelity method is proposed to reduce the computational cost of the former algorithms while retaining a similar accuracy. The adaptive GMM method is in this case run on a low-fidelity dynamical model, and only the expected values of the kernels are propagated point-wise in high-fidelity dynamics to compute a posteriori correction of the low-fidelity state pdf. If the former methods deal with the propagation of an initial uncertainty through a deterministic dynamical model, the effects of mismodeled or unmodeled forces are finally considered to further enhance the realism of the propagated statistics. In this case, the multifidelity GMM method is used at first to propagate the initial uncertainty through a low-fidelity, deterministic dynamical model. The point-wise propagations are then replaced with a DA-based algorithm to efficiently propagate a polynomial representation of the moments of the pdf in a stochastic dynamical system. These moments model the effects of stochastic accelerations on the deterministic kernels’ means, and coupled with the former GMM provide a description of the propagated state pdf that accounts for both the uncertainty in the initial state and the effects of neglected forces. The proposed methods are applied to the problem of orbit UP, and their performance is assessed in different orbital regimes. The results demonstrate the effectiveness of these methods in accurately propagating the initial uncertainty and the effects of process noise at a fraction of the computational cost of high-fidelity MC simulations. The LOADS method is then employed to solve the initial orbit determination (IOD) problem by exploiting the information on measurement uncertainty and to develop a preprocessing scheme aimed at improving the robustness of batch OD algorithms. These tools are finally validated on a set of real observations for an object in geostationary transfer orbit (GTO)
Bryson, Dean Edward. "A Unified, Multifidelity Quasi-Newton Optimization Method with Application to Aero-Structural Design." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1510146591195367.
De, lozzo Matthias. "Modèles de substitution spatio-temporels et multifidélité : Application à l'ingénierie thermique." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0027/document.
This PhD thesis deals with the construction of surrogate models in transient and steady states in the context of thermal simulation, with a few observations and many outputs.First, we design a robust construction of recurrent multilayer perceptron so as to approach a spatio-temporal dynamic. We use an average of neural networks resulting from a cross-validation procedure, whose associated data splitting allows to adjust the parameters of these models thanks to a test set without any information loss. Moreover, the construction of this perceptron can be distributed according to its outputs. This construction is applied to the modelling of the temporal evolution of the temperature at different points of an aeronautical equipment.Then, we proposed a mixture of Gaussian process models in a multifidelity framework where we have a high-fidelity observation model completed by many observation models with lower and no comparable fidelities. A particular attention is paid to the specifications of trends and adjustement coefficients present in these models. Different kriging and co-krigings models are put together according to a partition or a weighted aggregation based on a robustness measure associated to the most reliable design points. This approach is used in order to model the temperature at different points of the equipment in steady state.Finally, we propose a penalized criterion for the problem of heteroscedastic regression. This tool is build in the case of projection estimators and applied with the Haar wavelet. We also give some numerical results for different noise specifications and possible dependencies in the observations
(7033289), Viraj Dipakbhai Gandhi. "PARAMETRIC DESIGNS AND WEIGHT OPTIMIZATION USING DIRECT AND INDIRECT AERO-STRUCTURE LOAD TRANSFER METHODS." Thesis, 2019.
Частини книг з теми "Multifidelity models":
Van Buren, Kendra, and François Hemez. "Robust-Optimal Design Using Multifidelity Models." In Model Validation and Uncertainty Quantification, Volume 3, 199–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_21.
Eldred, Michael S., Leo W. T. Ng, Matthew F. Barone, and Stefan P. Domino. "Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models." In Handbook of Uncertainty Quantification, 991–1036. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_25.
Eldred, Michael S., Leo W. T. Ng, Matthew F. Barone, and Stefan P. Domino. "Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models." In Handbook of Uncertainty Quantification, 1–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_25-1.
Тези доповідей конференцій з теми "Multifidelity models":
Reuter, Bryan, Gianluca Geraci, Timothy Wildey, and Michael Eldred. "Multifidelity Uncertainty Quantification For Non-Deterministic Models." In Proposed for presentation at the ECCOMAS Congress 2022 held June 5-9, 2022 in Oslo, Norway. US DOE, 2022. http://dx.doi.org/10.2172/2003426.
Rezaeiravesh, S., R. Vinuesa, and P. Schlatter. "Towards Multifidelity Models with Calibration for Turbulent Flows." In 14th WCCM-ECCOMAS Congress. CIMNE, 2021. http://dx.doi.org/10.23967/wccm-eccomas.2020.348.
Cocco, Alessandro, and Alberto Savino. "Tiltrotor Whirl-Flutter Assessment by Multifidelity Aerodynamic Models." In AIAA SCITECH 2024 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-1850.
Chell, Brian, Steven Hoffenson, and Mark R. Blackburn. "Comparing Multifidelity Model Management Strategies for Multidisciplinary Design Optimization." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97859.
Robinson, Theresa, Michael Eldred, Karen Willcox, and Robert Haimes. "Strategies for Multifidelity Optimization with Variable Dimensional Hierarchical Models." In 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1819.
Reuter, Bryan, Gianluca Geraci, and Timothy Wildey. "Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models." In Proposed for presentation at the SIAM UQ 2022 held April 12-15, 2022 in Atlanta, GA US. US DOE, 2022. http://dx.doi.org/10.2172/2002277.
Karali, Hasan, Gokhan Inalhan, and Antonios Tsourdos. "AI-Based Multifidelity Surrogate Models to Develop Next Generation Modular UCAVs." In AIAA SCITECH 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-0670.
Li, Wu, and Karl Geiselhart. "Multiobjective Multidisciplinary Optimization of Low-Boom Supersonic Transports Using Multifidelity Models." In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-2097.
Guo, Zhendong, Wei Sun, Liming Song, Jun Li, and Zhenping Feng. "Generative Transfer Optimization for Aerodynamic Design." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-225.
Eldred, Michael, and Daniel Dunlavy. "Formulations for Surrogate-Based Optimization with Data Fit, Multifidelity, and Reduced-Order Models." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7117.
Звіти організацій з теми "Multifidelity models":
Blonigan, Patrick Joseph, Gianluca Geraci, Francesco Rizzi, Michael S. Eldred, and Kevin Carlberg. On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1567834.
Turinsky, Paul. Development of Adaptive Model Refinement (AMoR) for Multiphysics and Multifidelity Problems. Office of Scientific and Technical Information (OSTI), February 2015. http://dx.doi.org/10.2172/1169938.
Hough, Patricia Diane, Genetha Anne Gray, Joseph Pete Jr Castro, .), and Anthony Andrew Giunta. Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/877137.