Artigos de revistas sobre o tema "Multifidelity models"
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Molléro, Roch, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache e Maxime Sermesant. "Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models". Biomechanics and Modeling in Mechanobiology 17, n.º 1 (11 de setembro de 2017): 285–300. http://dx.doi.org/10.1007/s10237-017-0960-0.
Texto completo da fonteJacobs, Ryan, Philip E. Goins e Dane Morgan. "Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap". Machine Learning: Science and Technology 4, n.º 4 (1 de dezembro de 2023): 045060. http://dx.doi.org/10.1088/2632-2153/ad1627.
Texto completo da fonteNarayan, Akil, Claude Gittelson e Dongbin Xiu. "A Stochastic Collocation Algorithm with Multifidelity Models". SIAM Journal on Scientific Computing 36, n.º 2 (janeiro de 2014): A495—A521. http://dx.doi.org/10.1137/130929461.
Texto completo da fontePeng, Yijie, Jie Xu, Loo Hay Lee, Jianqiang Hu e Chun-Hung Chen. "Efficient Simulation Sampling Allocation Using Multifidelity Models". IEEE Transactions on Automatic Control 64, n.º 8 (agosto de 2019): 3156–69. http://dx.doi.org/10.1109/tac.2018.2886165.
Texto completo da fonteJasa, John, Pietro Bortolotti, Daniel Zalkind e Garrett Barter. "Effectively using multifidelity optimization for wind turbine design". Wind Energy Science 7, n.º 3 (11 de maio de 2022): 991–1006. http://dx.doi.org/10.5194/wes-7-991-2022.
Texto completo da fonteRumpfkeil, Markus P., e Philip Beran. "Construction of Dynamic Multifidelity Locally Optimized Surrogate Models". AIAA Journal 55, n.º 9 (setembro de 2017): 3169–79. http://dx.doi.org/10.2514/1.j055834.
Texto completo da fonteZhu, Xueyu, Akil Narayan e Dongbin Xiu. "Computational Aspects of Stochastic Collocation with Multifidelity Models". SIAM/ASA Journal on Uncertainty Quantification 2, n.º 1 (janeiro de 2014): 444–63. http://dx.doi.org/10.1137/130949154.
Texto completo da fonteKeshavarzzadeh, Vahid, Robert M. Kirby e Akil Narayan. "Convergence Acceleration for Time-Dependent Parametric Multifidelity Models". SIAM Journal on Numerical Analysis 57, n.º 3 (janeiro de 2019): 1344–68. http://dx.doi.org/10.1137/18m1170339.
Texto completo da fonteHoward, Amanda, Yucheng Fu e Panos Stinis. "A multifidelity approach to continual learning for physical systems". Machine Learning: Science and Technology 5, n.º 2 (16 de maio de 2024): 025042. http://dx.doi.org/10.1088/2632-2153/ad45b2.
Texto completo da fontePienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems". Biomedical Engineering and Computational Biology 9 (janeiro de 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.
Texto completo da fonteAhmed, Shady E., Omer San, Kursat Kara, Rami Younis e Adil Rasheed. "Multifidelity computing for coupling full and reduced order models". PLOS ONE 16, n.º 2 (11 de fevereiro de 2021): e0246092. http://dx.doi.org/10.1371/journal.pone.0246092.
Texto completo da fonteDhulipala, Somayajulu L. N., Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields, Andrew E. Slaughter, Zachary M. Prince, Vincent M. Labouré, Chandrakanth Bolisetti e Promit Chakroborty. "Accelerated statistical failure analysis of multifidelity TRISO fuel models". Journal of Nuclear Materials 563 (maio de 2022): 153604. http://dx.doi.org/10.1016/j.jnucmat.2022.153604.
Texto completo da fonteSrivastava, Shobhit, e Nathan Michael. "Efficient, Multifidelity Perceptual Representations via Hierarchical Gaussian Mixture Models". IEEE Transactions on Robotics 35, n.º 1 (fevereiro de 2019): 248–60. http://dx.doi.org/10.1109/tro.2018.2878363.
Texto completo da fonteVo, Huy D., Zachary Fox, Ania Baetica e Brian Munsky. "Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models". Journal of Physical Chemistry B 123, n.º 10 (19 de fevereiro de 2019): 2217–34. http://dx.doi.org/10.1021/acs.jpcb.8b10946.
Texto completo da fontePeherstorfer, Benjamin. "Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models". SIAM/ASA Journal on Uncertainty Quantification 7, n.º 2 (janeiro de 2019): 579–603. http://dx.doi.org/10.1137/17m1159208.
Texto completo da fonteDu, Xiaosong, Jie Ren e Leifur Leifsson. "Aerodynamic inverse design using multifidelity models and manifold mapping". Aerospace Science and Technology 85 (fevereiro de 2019): 371–85. http://dx.doi.org/10.1016/j.ast.2018.12.008.
Texto completo da fonteZeng, Xiaoshu, Gianluca Geraci, Michael S. Eldred, John D. Jakeman, Alex A. Gorodetsky e Roger Ghanem. "Multifidelity uncertainty quantification with models based on dissimilar parameters". Computer Methods in Applied Mechanics and Engineering 415 (outubro de 2023): 116205. http://dx.doi.org/10.1016/j.cma.2023.116205.
Texto completo da fonteMu, Weiyan, Qiuyue Wei, Dongli Cui e Shifeng Xiong. "Best Linear Unbiased Prediction for Multifidelity Computer Experiments". Mathematical Problems in Engineering 2018 (7 de junho de 2018): 1–7. http://dx.doi.org/10.1155/2018/8525736.
Texto completo da fonteRumpfkeil, Markus P., e Philip S. Beran. "Multifidelity Sparse Polynomial Chaos Surrogate Models Applied to Flutter Databases". AIAA Journal 58, n.º 3 (março de 2020): 1292–303. http://dx.doi.org/10.2514/1.j058452.
Texto completo da fonteSingh, Gulshan, e Ramana V. Grandhi. "Mixed-Variable Optimization Strategy Employing Multifidelity Simulation and Surrogate Models". AIAA Journal 48, n.º 1 (janeiro de 2010): 215–23. http://dx.doi.org/10.2514/1.43469.
Texto completo da fonteXing, W., M. Razi, R. M. Kirby, K. Sun e A. A. Shah. "Greedy nonlinear autoregression for multifidelity computer models at different scales". Energy and AI 1 (agosto de 2020): 100012. http://dx.doi.org/10.1016/j.egyai.2020.100012.
Texto completo da fontePerry, Daniel J., Robert M. Kirby, Akil Narayan e Ross T. Whitaker. "Allocation Strategies for High Fidelity Models in the Multifidelity Regime". SIAM/ASA Journal on Uncertainty Quantification 7, n.º 1 (janeiro de 2019): 203–31. http://dx.doi.org/10.1137/17m1144714.
Texto completo da fonteYin, Faliang, Xiaoming Xue, Chengze Zhang, Kai Zhang, Jianfa Han, BingXuan Liu, Jian Wang e Jun Yao. "Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization". SPE Journal 26, n.º 04 (21 de janeiro de 2021): 1614–35. http://dx.doi.org/10.2118/205013-pa.
Texto completo da fonteShi, Yan, Zhiqiang Wan, Zhigang Wu e Chao Yang. "Nonlinear Unsteady Aerodynamics Reduced Order Model of Airfoils Based on Algorithm Fusion and Multifidelity Framework". International Journal of Aerospace Engineering 2021 (16 de setembro de 2021): 1–26. http://dx.doi.org/10.1155/2021/4368104.
Texto completo da fonteSeo, Jongmin, Casey Fleeter, Andrew M. Kahn, Alison L. Marsden e Daniele E. Schiavazzi. "MULTIFIDELITY ESTIMATORS FOR CORONARY CIRCULATION MODELS UNDER CLINICALLY INFORMED DATA UNCERTAINTY". International Journal for Uncertainty Quantification 10, n.º 5 (2020): 449–66. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2020033068.
Texto completo da fonteRoderick, Oleg, Mihai Anitescu e Yulia Peet. "Proper orthogonal decompositions in multifidelity uncertainty quantification of complex simulation models". International Journal of Computer Mathematics 91, n.º 4 (20 de janeiro de 2014): 748–69. http://dx.doi.org/10.1080/00207160.2013.844431.
Texto completo da fonteSinsbeck, Michael, e Daniel M. Tartakovsky. "Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models". SIAM/ASA Journal on Uncertainty Quantification 3, n.º 1 (janeiro de 2015): 954–68. http://dx.doi.org/10.1137/141001743.
Texto completo da fonteNagawkar, Jethro, Jie Ren, Xiaosong Du, Leifur Leifsson e Slawomir Koziel. "Single- and Multipoint Aerodynamic Shape Optimization Using Multifidelity Models and Manifold Mapping". Journal of Aircraft 58, n.º 3 (maio de 2021): 591–608. http://dx.doi.org/10.2514/1.c035297.
Texto completo da fonteAmrit, Anand, Leifur Leifsson e Slawomir Koziel. "Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models". Journal of Aircraft 57, n.º 3 (maio de 2020): 388–98. http://dx.doi.org/10.2514/1.c035500.
Texto completo da fonteClare, Mariana C. A., Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter e Matthew D. Piggott. "Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding". Natural Hazards and Earth System Sciences 22, n.º 8 (3 de agosto de 2022): 2491–515. http://dx.doi.org/10.5194/nhess-22-2491-2022.
Texto completo da fonteRobinson, T. D., M. S. Eldred, K. E. Willcox e R. Haimes. "Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping". AIAA Journal 46, n.º 11 (novembro de 2008): 2814–22. http://dx.doi.org/10.2514/1.36043.
Texto completo da fonteChan, F. T. S., A. Chaube, V. Mohan, V. Arora e M. K. Tiwari. "Operation allocation in automated manufacturing system using GA-based approach with multifidelity models". Robotics and Computer-Integrated Manufacturing 26, n.º 5 (outubro de 2010): 526–34. http://dx.doi.org/10.1016/j.rcim.2010.04.002.
Texto completo da fonteAllaire, Douglas, e Karen Willcox. "A MATHEMATICAL AND COMPUTATIONAL FRAMEWORK FOR MULTIFIDELITY DESIGN AND ANALYSIS WITH COMPUTER MODELS". International Journal for Uncertainty Quantification 4, n.º 1 (2014): 1–20. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2013004121.
Texto completo da fonteKontaxoglou, Anastasios, Seiji Tsutsumi, Samir Khan e Shinichi Nakasuka. "Towards a Digital Twin Enabled Multifidelity Framework for Small Satellites". PHM Society European Conference 6, n.º 1 (29 de junho de 2021): 10. http://dx.doi.org/10.36001/phme.2021.v6i1.2801.
Texto completo da fonteMessina, Luca, Alessio Quaglino, Alexandra Goryaeva, Mihai-Cosmin Marinica, Christophe Domain, Nicolas Castin, Giovanni Bonny e Rolf Krause. "A DFT-driven multifidelity framework for constructing efficient energy models for atomic-scale simulations". Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 483 (novembro de 2020): 15–21. http://dx.doi.org/10.1016/j.nimb.2020.09.011.
Texto completo da fonteGruber, Anthony, Max Gunzburger, Lili Ju, Rihui Lan e Zhu Wang. "Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling". Geoscientific Model Development 16, n.º 4 (21 de fevereiro de 2023): 1213–29. http://dx.doi.org/10.5194/gmd-16-1213-2023.
Texto completo da fonteBerci, Marco, e Francesco Torrigiani. "Multifidelity Sensitivity Study of Subsonic Wing Flutter for Hybrid Approaches in Aircraft Multidisciplinary Design and Optimisation". Aerospace 7, n.º 11 (12 de novembro de 2020): 161. http://dx.doi.org/10.3390/aerospace7110161.
Texto completo da fonteHebert, James L., Thomas H. Holzer, Timothy J. Eveleigh e Shahryar Sarkani. "Use of Multifidelity and Surrogate Models in the Design and Development of Physics-Based Systems". Systems Engineering 19, n.º 4 (julho de 2016): 375–91. http://dx.doi.org/10.1002/sys.21346.
Texto completo da fonteXu, C., Z. Liu, B. T. Cao, G. Meschke e X. Liu. "Multifidelity operator learning for predicting displacement fields of tunnel linings under external loads". IOP Conference Series: Earth and Environmental Science 1333, n.º 1 (1 de maio de 2024): 012045. http://dx.doi.org/10.1088/1755-1315/1333/1/012045.
Texto completo da fonteAbraham, Troy, David Lazzara e Douglas Hunsaker. "Multifidelity Comparison of Supersonic Wave Drag Prediction Methods Using Axisymmetric Bodies". Aerospace 11, n.º 5 (30 de abril de 2024): 359. http://dx.doi.org/10.3390/aerospace11050359.
Texto completo da fonteJiang, Zhenxiang, Jongeun Choi e Seungik Baek. "Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications". Computers in Biology and Medicine 133 (junho de 2021): 104394. http://dx.doi.org/10.1016/j.compbiomed.2021.104394.
Texto completo da fonteBerends, K. D., F. Scheel, J. J. Warmink, W. P. de Boer, R. Ranasinghe e S. J. M. H. Hulscher. "Towards efficient uncertainty quantification with high-resolution morphodynamic models: A multifidelity approach applied to channel sedimentation". Coastal Engineering 152 (outubro de 2019): 103520. http://dx.doi.org/10.1016/j.coastaleng.2019.103520.
Texto completo da fonteBerci, M., P. H. Gaskell, R. W. Hewson e V. V. Toropov. "Multifidelity metamodel building as a route to aeroelastic optimization of flexible wings". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, n.º 9 (5 de julho de 2011): 2115–37. http://dx.doi.org/10.1177/0954406211403549.
Texto completo da fonteReed, John A., e Abdollah A. Afjeh. "Computational Simulation of Gas Turbines: Part 1—Foundations of Component-Based Models". Journal of Engineering for Gas Turbines and Power 122, n.º 3 (15 de maio de 2000): 366–76. http://dx.doi.org/10.1115/1.1287490.
Texto completo da fonteNguyen, Vinh-Tan, Jason Yu Chuan Leong, Satoshi Watanabe, Toshimitsu Morooka e Takayuki Shimizu. "A Multi-Fidelity Model for Simulations and Sensitivity Analysis of Piezoelectric Inkjet Printheads". Micromachines 12, n.º 9 (29 de agosto de 2021): 1038. http://dx.doi.org/10.3390/mi12091038.
Texto completo da fonteMarques, Simão, Lucas Kob, Trevor T. Robinson e Weigang Yao. "Nonintrusive Aerodynamic Shape Optimisation with a POD-DEIM Based Trust Region Method". Aerospace 10, n.º 5 (17 de maio de 2023): 470. http://dx.doi.org/10.3390/aerospace10050470.
Texto completo da fonteCarpenter, Chris. "Digital-Twin Approach Predicts Fatigue Damage of Marine Risers". Journal of Petroleum Technology 73, n.º 10 (1 de outubro de 2021): 65–66. http://dx.doi.org/10.2118/1021-0065-jpt.
Texto completo da fonteRehme, Michael, Stephen Roberts e Dirk Pflüger. "Uncertainty quantification for the Hokkaido Nansei-Oki tsunami using B-splines on adaptive sparse grids". ANZIAM Journal 62 (29 de junho de 2021): C30—C44. http://dx.doi.org/10.21914/anziamj.v62.16121.
Texto completo da fonteAdjei, Richard Amankwa, Xinqian Zheng, Fangyuan Lou e Chuang Ding. "Multifidelity Optimization Under Uncertainty for Robust Design of a Micro-Turbofan Turbine Stage". Journal of Engineering for Gas Turbines and Power, 16 de agosto de 2022. http://dx.doi.org/10.1115/1.4055231.
Texto completo da fonteKontaxoglou, Anastasios, Seiji Tsutsumi, Samir Khan e Shinichi Nakasuka. "Multifidelity Framework for Small Satellite Thermal Analysis". Journal of Spacecraft and Rockets, 13 de setembro de 2023, 1–11. http://dx.doi.org/10.2514/1.a35666.
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