Inscrição na biblioteca: Guest
International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY

Volume 4, Edição 5, 2014, pp. 365-386
DOI: 10.1615/Int.J.UncertaintyQuantification.2014006914
Get accessDownload

RESUMO

We consider in this paper the problem of building a fast-running approximation−also called surrogate model−of a complex computer code. The co-kriging based surrogate model is a promising tool to build such an approximation when the complex computer code can be run at different levels of accuracy. We present here an original approach to perform a multi-fidelity co-kriging model which is based on a recursive formulation. We prove that the predictive mean and the variance of the presented approach are identical to the ones of the original co-kriging model. However, our new approach allows to obtain original results. First, closed-form formulas for the universal co-kriging predictive mean and variance are given. Second, a fast cross-validation procedure for the multi-fidelity co-kriging model is introduced. Finally, the proposed approach has a reduced computational complexity compared to the previous one. The multi-fidelity model is successfully applied to emulate a hydrodynamic simulator.

CITADO POR
  1. Chen Shishi, Jiang Zhen, Yang Shuxing, Apley Daniel W., Chen Wei, Nonhierarchical multi‐model fusion using spatial random processes, International Journal for Numerical Methods in Engineering, 106, 7, 2016. Crossref

  2. Perdikaris P., Venturi D., Royset J. O., Karniadakis G. E., Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 471, 2179, 2015. Crossref

  3. Zaytsev Alexey, Reliable surrogate modeling of engineering data with more than two levels of fidelity, 2016 7th International Conference on Mechanical and Aerospace Engineering (ICMAE), 2016. Crossref

  4. Pilania G., Gubernatis J.E., Lookman T., Multi-fidelity machine learning models for accurate bandgap predictions of solids, Computational Materials Science, 129, 2017. Crossref

  5. Perdikaris Paris, Karniadakis George Em, Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond, Journal of The Royal Society Interface, 13, 118, 2016. Crossref

  6. Bartoli Nathalie, Bouhlel Mohamed-Amine, Kurek Igor, Lafage Rémi, Lefebvre Thierry, Morlier Joseph, Priem Rémy, Stilz Vivien, Regis Rommel, Improvement of efficient global optimization with application to aircraft wing design, 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2016. Crossref

  7. Babaee H., Perdikaris P., Chryssostomidis C., Karniadakis G. E., Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations, Journal of Fluid Mechanics, 809, 2016. Crossref

  8. Habib Ahsanul, Singh Hemant Kumar, Ray Tapabrata, A multiple surrogate assisted evolutionary algorithm for optimization involving iterative solvers, Engineering Optimization, 50, 9, 2018. Crossref

  9. Benamara Tariq, Breitkopf Piotr, Lepot Ingrid, Sainvitu Caroline, Villon Pierre, Multi-fidelity POD surrogate-assisted optimization: Concept and aero-design study, Structural and Multidisciplinary Optimization, 56, 6, 2017. Crossref

  10. Perdikaris P., Raissi M., Damianou A., Lawrence N. D., Karniadakis G. E., Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473, 2198, 2017. Crossref

  11. Liu Haitao, Ong Yew-Soon, Cai Jianfei, Wang Yi, Cope with diverse data structures in multi-fidelity modeling: A Gaussian process method, Engineering Applications of Artificial Intelligence, 67, 2018. Crossref

  12. Ezzat Ahmed Aziz, Pourhabib Arash, Ding Yu, Sequential Design for Functional Calibration of Computer Models, Technometrics, 60, 3, 2018. Crossref

  13. Parussini L., Venturi D., Perdikaris P., Karniadakis G.E., Multi-fidelity Gaussian process regression for prediction of random fields, Journal of Computational Physics, 336, 2017. Crossref

  14. Singh Prashant, Couckuyt Ivo, Elsayed Khairy, Deschrijver Dirk, Dhaene Tom, Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models, Journal of Optimization Theory and Applications, 175, 1, 2017. Crossref

  15. Pan Wenxiao, Yang Xiu, Bao Jie, Wang Michelle, Optimizing Discharge Capacity of Li-O2Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization, Journal of The Electrochemical Society, 164, 11, 2017. Crossref

  16. Xiao Manyu, Zhang Guohua, Breitkopf Piotr, Villon Pierre, Zhang Weihong, Extended Co-Kriging interpolation method based on multi-fidelity data, Applied Mathematics and Computation, 323, 2018. Crossref

  17. Pang Guofei, Perdikaris Paris, Cai Wei, Karniadakis George Em, Discovering variable fractional orders of advection–dispersion equations from field data using multi-fidelity Bayesian optimization, Journal of Computational Physics, 348, 2017. Crossref

  18. Liu Haitao, Ong Yew-Soon, Cai Jianfei, A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design, Structural and Multidisciplinary Optimization, 57, 1, 2018. Crossref

  19. Zhou Qi, Wang Yan, Choi Seung-Kyum, Jiang Ping, Shao Xinyu, Hu Jiexiang, Shu Leshi, A robust optimization approach based on multi-fidelity metamodel, Structural and Multidisciplinary Optimization, 57, 2, 2018. Crossref

  20. Bonfiglio Luca, Perdikaris Paris, Brizzolara Stefano, Karniadakis George, A multi-fidelity framework for investigating the performance of super-cavitating hydrofoils under uncertain flow conditions, 19th AIAA Non-Deterministic Approaches Conference, 2017. Crossref

  21. Gubernatis J. E., Lookman T., Machine learning in materials design and discovery: Examples from the present and suggestions for the future, Physical Review Materials, 2, 12, 2018. Crossref

  22. Bonfiglio Luca, Perdikaris Paris, Águila Jose, Karniadakis George E., A probabilistic framework for multidisciplinary design: Application to the hydrostructural optimization of supercavitating hydrofoils, International Journal for Numerical Methods in Engineering, 116, 4, 2018. Crossref

  23. Ghoreishi Seyede Fatemeh, Allaire Douglas L., Gaussian Process Regression for Bayesian Fusion of Multi-Fidelity Information Sources, 2018 Multidisciplinary Analysis and Optimization Conference, 2018. Crossref

  24. Liu Haitao, Cai Jianfei, Ong Yew-Soon, Remarks on multi-output Gaussian process regression, Knowledge-Based Systems, 144, 2018. Crossref

  25. Hampton Jerrad, Fairbanks Hillary R., Narayan Akil, Doostan Alireza, Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction, Journal of Computational Physics, 368, 2018. Crossref

  26. Zhang Jiangjiang, Man Jun, Lin Guang, Wu Laosheng, Zeng Lingzao, Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations, Water Resources Research, 54, 7, 2018. Crossref

  27. Lo Charles, Chow Paul, Multi-fidelity Optimization for High-Level Synthesis Directives, 2018 28th International Conference on Field Programmable Logic and Applications (FPL), 2018. Crossref

  28. Abdallah Imad, Lataniotis Christos, Sudret Bruno, Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators — Application to extreme loads on wind turbines, Probabilistic Engineering Mechanics, 55, 2019. Crossref

  29. Zhang Dongkun, Yang Liu, Karniadakis George Em, Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors, Journal of Computational Physics, 374, 2018. Crossref

  30. Ranftl Sascha, Melito Gian Marco, Badeli Vahid, Reinbacher-Köstinger Alice, Ellermann Katrin, von der Linden Wolfgang, Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection, Entropy, 22, 1, 2019. Crossref

  31. Jiang Ping, Zhou Qi, Shao Xinyu, Multi-fidelity Surrogate Models, in Surrogate Model-Based Engineering Design and Optimization, 2020. Crossref

  32. Chocat Rudy, Beaucaire Paul, Debeugny Loïc, Lefebvre Jean-Pierre, Sainvitu Caroline, Breitkopf Piotr, Wyart Eric, Damage tolerance reliability analysis combining Kriging regression and support vector machine classification, Engineering Fracture Mechanics, 216, 2019. Crossref

  33. Zheng Qiang, Zhang Jiangjiang, Xu Wenjie, Wu Laosheng, Zeng Lingzao, Adaptive Multifidelity Data Assimilation for Nonlinear Subsurface Flow Problems, Water Resources Research, 55, 1, 2019. Crossref

  34. Marque-Pucheu Sophie, Perrin Guillaume, Garnier Josselin, Efficient sequential experimental design for surrogate modeling of nested codes, ESAIM: Probability and Statistics, 23, 2019. Crossref

  35. Yang Xiu, Barajas-Solano David, Tartakovsky Guzel, Tartakovsky Alexandre M., Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence, Journal of Computational Physics, 395, 2019. Crossref

  36. Jakeman John D., Eldred Michael S., Geraci Gianluca, Gorodetsky Alex, Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis, International Journal for Numerical Methods in Engineering, 121, 6, 2020. Crossref

  37. Absi Ghina N., Mahadevan Sankaran, Simulation Resource Optimization for Multi-Fidelity Model Calibration, AIAA Scitech 2019 Forum, 2019. Crossref

  38. Gahrooei Mostafa Reisi, Paynabar Kamaran, Pacella Massimo, Colosimo Bianca Maria, An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data, IISE Transactions, 51, 11, 2019. Crossref

  39. Absi Ghina N., Mahadevan Sankaran, Simulation and Sensor Optimization for Multifidelity Dynamics Model Calibration, AIAA Journal, 58, 2, 2020. Crossref

  40. Haugen Matz A., Stein Michael L., Sriver Ryan L., Moyer Elisabeth J., Future climate emulations using quantile regressions on large ensembles, Advances in Statistical Climatology, Meteorology and Oceanography, 5, 1, 2019. Crossref

  41. Bonfiglio L., Perdikaris P., Brizzolara S., Karniadakis G.E., Multi-fidelity optimization of super-cavitating hydrofoils, Computer Methods in Applied Mechanics and Engineering, 332, 2018. Crossref

  42. Nachar Stéphane, Boucard Pierre-Alain, Néron David, Bordeu Felipe, Coupling multi-fidelity kriging and model-order reduction for the construction of virtual charts, Computational Mechanics, 64, 6, 2019. Crossref

  43. Cheng Kai, Lu Zhenzhou, Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation, International Journal for Numerical Methods in Engineering, 121, 9, 2020. Crossref

  44. Shu Leshi, Jiang Ping, Song Xueguan, Zhou Qi, Novel Approach for Selecting Low-Fidelity Scale Factor in Multifidelity Metamodeling, AIAA Journal, 57, 12, 2019. Crossref

  45. Klyuchnikov Nikita, Burnaev Evgeny, Gaussian process classification for variable fidelity data, Neurocomputing, 397, 2020. Crossref

  46. Biehler Jonas, Mäck Markus, Nitzler Jonas, Hanss Michael, Koutsourelakis Phaedon‐Stelios, Wall Wolfgang A., Multifidelity approaches for uncertainty quantification, GAMM-Mitteilungen, 42, 2, 2019. Crossref

  47. Fernández-Godino M. Giselle, Dubreuil Sylvain, Bartoli Nathalie, Gogu Christian, Balachandar S., Haftka Raphael T., Linear regression-based multifidelity surrogate for disturbance amplification in multiphase explosion, Structural and Multidisciplinary Optimization, 60, 6, 2019. Crossref

  48. Sarkar Soumalya, Mondal Sudeepta, Joly Michael, Lynch Matthew E., Bopardikar Shaunak D., Acharya Ranadip, Perdikaris Paris, Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration, Journal of Mechanical Design, 141, 12, 2019. Crossref

  49. Talapatra Anjana, Boluki Shahin, Honarmandi Pejman, Solomou Alexandros, Zhao Guang, Ghoreishi Seyede Fatemeh, Molkeri Abhilash, Allaire Douglas, Srivastava Ankit, Qian Xiaoning, Dougherty Edward R., Lagoudas Dimitris C., Arróyave Raymundo, Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales, Frontiers in Materials, 6, 2019. Crossref

  50. Lee Seungjoon, Dietrich Felix, Karniadakis George E., Kevrekidis Ioannis G., Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion, Interface Focus, 9, 3, 2019. Crossref

  51. Tran Anh, Wildey Tim, McCann Scott, sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications, Journal of Computing and Information Science in Engineering, 20, 3, 2020. Crossref

  52. Cheng Kai, Lu Zhenzhou, Ling Chunyan, Zhou Suting, Surrogate-assisted global sensitivity analysis: an overview, Structural and Multidisciplinary Optimization, 61, 3, 2020. Crossref

  53. Zheng Hongyu, Xie Fangfang, Ji Tingwei, Zhu Zaoxu, Zheng Yao, Multifidelity kinematic parameter optimization of a flapping airfoil, Physical Review E, 101, 1, 2020. Crossref

  54. Pilania Ghanshyam, Balachandran Prasanna V., Gubernatis James E., Lookman Turab, Data-Based Methods for Materials Design and Discovery, 2020. Crossref

  55. Wang Yan, McDowell David L., Uncertainty quantification in materials modeling, in Uncertainty Quantification in Multiscale Materials Modeling, 2020. Crossref

  56. Babaee H., Bastidas C., DeFilippo M., Chryssostomidis C., Karniadakis G. E., A Multifidelity Framework and Uncertainty Quantification for Sea Surface Temperature in the Massachusetts and Cape Cod Bays, Earth and Space Science, 7, 2, 2020. Crossref

  57. Zhou Qi, Wu Yuda, Guo Zhendong, Hu Jiexiang, Jin Peng, A generalized hierarchical co-Kriging model for multi-fidelity data fusion, Structural and Multidisciplinary Optimization, 62, 4, 2020. Crossref

  58. Brevault Loïc, Balesdent Mathieu, Hebbal Ali, Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities, application to aerospace systems, Aerospace Science and Technology, 107, 2020. Crossref

  59. Xu Yueqi, Song Xueguan, Zhang Chao, Hierarchical regression framework for multi-fidelity modeling, Knowledge-Based Systems, 212, 2021. Crossref

  60. Chakraborty Souvik, Transfer learning based multi-fidelity physics informed deep neural network, Journal of Computational Physics, 426, 2021. Crossref

  61. Ma Pulong, Objective Bayesian Analysis of a Cokriging Model for Hierarchical Multifidelity Codes, SIAM/ASA Journal on Uncertainty Quantification, 8, 4, 2020. Crossref

  62. Lafzi Ali, Dabiri Sadegh, Dynamics of droplet migration in oscillatory and pulsating microchannel flows and prediction and uncertainty quantification of its lateral equilibrium position using multifidelity Gaussian processes, Physics of Fluids, 33, 6, 2021. Crossref

  63. Hebbal Ali, Brevault Loïc, Balesdent Mathieu, Talbi El-Ghazali, Melab Nouredine, Multi-fidelity modeling with different input domain definitions using deep Gaussian processes, Structural and Multidisciplinary Optimization, 63, 5, 2021. Crossref

  64. Egorova Olga, Hafizi Roohollah, Woods David C., Day Graeme M., Multifidelity Statistical Machine Learning for Molecular Crystal Structure Prediction, The Journal of Physical Chemistry A, 124, 39, 2020. Crossref

  65. Muyskens Amanda, Schmidt Kathleen, Nelms Matthew, Barton Nathan, Florando Jeffrey, Kupresanin Ana, Rivera David, A practical extension of the recursive multi‐fidelity model for the emulation of hole closure experiments, Statistical Analysis and Data Mining: The ASA Data Science Journal, 14, 6, 2021. Crossref

  66. Ellison M., DiazDelaO F.A., Ince N.Z., Willetts M., Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation, Applied Mathematical Modelling, 100, 2021. Crossref

  67. Qian Jiachang, Cheng Yuansheng, Zhang Anfu, Zhou Qi, Zhang Jinlan, Optimization design of metamaterial vibration isolator with honeycomb structure based on multi-fidelity surrogate model, Structural and Multidisciplinary Optimization, 64, 1, 2021. Crossref

  68. Yoshida Tomohiro, Maezono Ryo, Hongo Kenta, Exploring Heat-Shielding Nanoparticle-Based Materials via First-Principles Calculations and Transfer Learning, ACS Applied Nano Materials, 4, 2, 2021. Crossref

  69. Lu Chi-Ken, Shafto Patrick, Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning, Entropy, 23, 11, 2021. Crossref

  70. Ruan Xiongfeng, Jiang Ping, Zhou Qi, Hu Jiexiang, Shu Leshi, Variable-fidelity probability of improvement method for efficient global optimization of expensive black-box problems, Structural and Multidisciplinary Optimization, 62, 6, 2020. Crossref

  71. Gorodetsky A. A., Jakeman J. D., Geraci G., MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources, Computational Mechanics, 68, 4, 2021. Crossref

  72. Geraci Gianluca, Eldred Michael S., Gorodetsky Alex, Jakeman John, Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project, AIAA Scitech 2019 Forum, 2019. Crossref

  73. Stanek Lucas J., Bopardikar Shaunak D., Murillo Michael S., Multifidelity regression of sparse plasma transport data available in disparate physical regimes, Physical Review E, 104, 6, 2021. Crossref

  74. Bhattacharjee Himaghna, Vlachos Dionisios G., Thermochemical Data Fusion Using Graph Representation Learning, Journal of Chemical Information and Modeling, 60, 10, 2020. Crossref

  75. Konomi Bledar A., Karagiannis Georgios, Bayesian Analysis of Multifidelity Computer Models With Local Features and Nonnested Experimental Designs: Application to the WRF Model, Technometrics, 63, 4, 2021. Crossref

  76. Zhou K., Tang J., Efficient characterization of dynamic response variation using multi-fidelity data fusion through composite neural network, Engineering Structures, 232, 2021. Crossref

  77. Ryou Gilhyun, Tal Ezra, Karaman Sertac, Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers, The International Journal of Robotics Research, 40, 12-14, 2021. Crossref

  78. Huang Bing, von Lilienfeld O. Anatole, Ab Initio Machine Learning in Chemical Compound Space, Chemical Reviews, 121, 16, 2021. Crossref

  79. Guo Zhendong, Wang Qineng, Song Liming, Li Jun, Parallel multi-fidelity expected improvement method for efficient global optimization, Structural and Multidisciplinary Optimization, 64, 3, 2021. Crossref

  80. Lopez-Caballero Fernando, Probabilistic seismic analysis for liquefiable embankment through multi-fidelity codes approach, Soil Dynamics and Earthquake Engineering, 149, 2021. Crossref

  81. Jin Yaochu, Wang Handing, Sun Chaoli, Knowledge Transfer in Data-Driven Evolutionary Optimization, in Data-Driven Evolutionary Optimization, 975, 2021. Crossref

  82. Guo Shuai, Silva Camilo F., Polifke Wolfgang, Robust identification of flame frequency response via multi-fidelity Gaussian process approach, Journal of Sound and Vibration, 502, 2021. Crossref

  83. Romor Francesco, Tezzele Marco, Rozza Gianluigi, Multi‐fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces, PAMM, 20, S1, 2021. Crossref

  84. Batra Rohit, Pilania Ghanshyam, Uberuaga Blas P., Ramprasad Rampi, Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia, ACS Applied Materials & Interfaces, 11, 28, 2019. Crossref

  85. Jin Seung-Seop, Kim Sung Tae, Park Young-Hwan, Combining point and distributed strain sensor for complementary data-fusion: A multi-fidelity approach, Mechanical Systems and Signal Processing, 157, 2021. Crossref

  86. Lin Quan, Hu Dawei, Hu Jiexiang, Cheng Yuansheng, Zhou Qi, A screening-based gradient-enhanced Gaussian process regression model for multi-fidelity data fusion, Advanced Engineering Informatics, 50, 2021. Crossref

  87. Wiens Avery E., Copan Andreas V., Schaefer Henry F., Multi-fidelity Gaussian process modeling for chemical energy surfaces, Chemical Physics Letters, 737, 2019. Crossref

  88. Zhang Sheng, Yang Xiu, Tindel Samy, Lin Guang, Augmented Gaussian random field: Theory and computation, Discrete & Continuous Dynamical Systems - S, 15, 4, 2022. Crossref

  89. Valladares Homero, Li Tianyi, Zhu Likun, El-Mounayri Hazim, Hashem Ahmed M., Abdel-Ghany Ashraf E., Tovar Andres, Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials, Journal of Power Sources, 528, 2022. Crossref

  90. Jia Bin, Xin Ming, Incremental Uncertainty Propagation with Multi-Fidelity Gaussian Process, AIAA SCITECH 2022 Forum, 2022. Crossref

  91. Pilania Ghanshyam, Balachandran Prasanna V., Gubernatis James E., Lookman Turab, Multi-Fidelity Learning, in Data-Based Methods for Materials Design and Discovery, 2020. Crossref

  92. Khatouri Hanane, Benamara Tariq, Breitkopf Piotr, Demange Jean, Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey, Advanced Modeling and Simulation in Engineering Sciences, 9, 1, 2022. Crossref

  93. Kaps Arne, Czech Catharina, Duddeck Fabian, A hierarchical kriging approach for multi-fidelity optimization of automotive crashworthiness problems, Structural and Multidisciplinary Optimization, 65, 4, 2022. Crossref

  94. Liu Xinwang, Zhao Weiwen, Wan Decheng, Multi-fidelity Co-Kriging surrogate model for ship hull form optimization, Ocean Engineering, 243, 2022. Crossref

  95. Stroh Rémi, Bect Julien, Demeyer Séverine, Fischer Nicolas, Marquis Damien, Vazquez Emmanuel, Sequential Design of Multi-Fidelity Computer Experiments: Maximizing the Rate of Stepwise Uncertainty Reduction, Technometrics, 64, 2, 2022. Crossref

  96. Seidl D. Thomas, Valiveti Dakshina M., Peridynamics and surrogate modeling of pressure-driven well stimulation, International Journal of Rock Mechanics and Mining Sciences, 154, 2022. Crossref

  97. Palar Pramudita S., Parussini Lucia, Bregant Luigi, Shimoyama Koji, Izzaturrahman Muhammad F., Baehaqi Febrian A., Zuhal Lavi, Composite Kernel Functions for Surrogate Modeling using Recursive Multi-Fidelity Kriging, AIAA SCITECH 2022 Forum, 2022. Crossref

  98. Ye Wenxing, Tan Matthias Hwai Yong, Multi‐fidelity Gaussian process modeling with boundary information, Applied Stochastic Models in Business and Industry, 38, 2, 2022. Crossref

  99. Menon Nandana, Mondal Sudeepta, Basak Amrita, Multi-Fidelity Surrogate-Based Process Mapping with Uncertainty Quantification in Laser Directed Energy Deposition, Materials, 15, 8, 2022. Crossref

  100. Petsagkourakis Panagiotis, Chachuat Benoit, Antonio del Rio-Chanona Ehecatl, Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes, 2021 60th IEEE Conference on Decision and Control (CDC), 2021. Crossref

  101. Zhang Lili, Wu Yuda, Jiang Ping, Choi Seung-Kyum, Zhou Qi, A multi-fidelity surrogate modeling approach for incorporating multiple non-hierarchical low-fidelity data, Advanced Engineering Informatics, 51, 2022. Crossref

  102. Thenon A., Gervais V., Le Ravalec M., Sequential design strategy for kriging and cokriging-based machine learning in the context of reservoir history-matching, Computational Geosciences, 26, 5, 2022. Crossref

  103. Newberry Felix, Hampton Jerrad, Jansen Kenneth, Doostan Alireza, Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification, Computational Mechanics, 69, 2, 2022. Crossref

  104. Korondi Péter Zénó, Marchi Mariapia, Parussini Lucia, Quagliarella Domenico, Poloni Carlo, Multi-Objective Design Optimisation of an Airfoil with Geometrical Uncertainties Leveraging Multi-Fidelity Gaussian Process Regression, in Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, 8, 2021. Crossref

  105. Prakash Supraj, Raman Venkatramanan, Multi-fidelity Modeling-based Estimation of Rotating Detonation Engine Performance, AIAA SCITECH 2022 Forum, 2022. Crossref

  106. Morales Elisa, Korondi Péter Zénó, Quagliarella Domenico, Tognaccini Renato, Marchi Mariapia, Parussini Lucia, Poloni Carlo, Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation, in Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, 8, 2021. Crossref

  107. Grassi Francesco, Manganini Giorgio, Garraffa Michele, Mainini Laura, Resource Aware Multifidelity Active Learning for Efficient Optimization, AIAA Scitech 2021 Forum, 2021. Crossref

  108. Howard Amanda A., Yu Tong, Wang Wei, Tartakovsky Alexandre M., Physics-informed CoKriging model of a redox flow battery, Journal of Power Sources, 542, 2022. Crossref

  109. Striegel Christoph, Biehler Jonas, Wall Wolfgang A., Kauermann Göran, A Multifidelity Function-on-Function Model Applied to an Abdominal Aortic Aneurysm, Technometrics, 64, 3, 2022. Crossref

  110. Kishi Yuki, Kanazaki Masahiro, Makino Yoshikazu, Supersonic Forward-Swept Wing Design Using Multifidelity Efficient Global Optimization, Journal of Aircraft, 59, 4, 2022. Crossref

  111. Sharma Somya, Thompson Marten, Laefer Debra, Lawler Michael, McIlhany Kevin, Pauluis Olivier, Trinkle Dallas R., Chatterjee Snigdhansu, Machine Learning Methods for Multiscale Physics and Urban Engineering Problems, Entropy, 24, 8, 2022. Crossref

  112. Li Zengcong, Zhang Shu, Li Hongqing, Tian Kuo, Cheng Zhizhong, Chen Yan, Wang Bo, On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks, Advanced Engineering Informatics, 53, 2022. Crossref

  113. Han Tianhong, Ahmed Kaleem S., Gosain Arun K., Tepole Adrian Buganza, Lee Taeksang, Multi-Fidelity Gaussian Process Surrogate Modeling of Pediatric Tissue Expansion, Journal of Biomechanical Engineering, 144, 12, 2022. Crossref

  114. Pidaparthi Bharath, Missoum Samy, A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Classification Inconsistency, Journal of Computing and Information Science in Engineering, 23, 1, 2023. Crossref

  115. Tsilifis Panagiotis, Pandita Piyush, Ghosh Sayan, Wang Liping, Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds, Entropy, 24, 9, 2022. Crossref

  116. Shi Maolin, Liang Zhenwei, Zhang Jian, Xu Lizhang, Song Xueguan, A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester, Structural and Multidisciplinary Optimization, 65, 9, 2022. Crossref

  117. Wan Hua‐Ping, Zhang Zi‐Nan, Luo Yaozhi, Ren Wei‐Xin, Todd Michael D., Analytical uncertainty quantification approach based on adaptive generalized co‐Gaussian process model , International Journal for Numerical Methods in Engineering, 2022. Crossref

  118. Freitas Rodolfo S.M., Lima Ágatha P.F., Chen Cheng, Rochinha Fernando A., Mira Daniel, Jiang Xi, Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models, Fuel, 329, 2022. Crossref

  119. Saunders Robert, Rawlings Anna, Birnbaum Andrew, Iliopoulos Athanasios, Michopoulos John, Lagoudas Dimitris, Elwany Alaa, Additive Manufacturing Melt Pool Prediction and Classification via Multifidelity Gaussian Process Surrogates, Integrating Materials and Manufacturing Innovation, 2022. Crossref

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain