Xiaosong Du

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	Све	Од 2020
Наводи	863	819
h-индекс	13	13
i10-индекс	14	14

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Leifur LeifssonAssociate Professor, School of Aeronautics and Astronautics, Purdue UniversityВерификована је имејл адреса на purdue.edu
Joaquim R. R. A. MartinsPauline M. Sherman Collegiate Professor of Aerospace Engineering, University of MichiganВерификована је имејл адреса на umich.edu
Slawomir KozielReykjavik UniversityВерификована је имејл адреса на ru.is
Ping HeIowa State UniversityВерификована је имејл адреса на iastate.edu
Jiming SongIowa State UniversityВерификована је имејл адреса на iastate.edu
Yonatan Afework TesfahunegnAssistant Professor, Department of Engineering, Reykjavik UniversityВерификована је имејл адреса на ru.is
Thomas O'Leary-RoseberryOden Institute, The University of Texas at AustinВерификована је имејл адреса на utexas.edu
Karen WillcoxOden Institute for Computational Engineering and Sciences, UT AustinВерификована је имејл адреса на oden.utexas.edu
Omar GhattasProfessor & Cockrell endowed chair, Mechanical Engineering & Oden Institute, Univ of Texas at AustinВерификована је имејл адреса на ices.utexas.edu
Andrew ThelenNASA Langley Research CenterВерификована је имејл адреса на nasa.gov
William Q. MeekerDistinguished Professor of Statistics at Iowa State UniversityВерификована је имејл адреса на iastate.edu
Simon LaflammeIowa State UniversityВерификована је имејл адреса на alum.mit.edu
An ChenIowa State UniversityВерификована је имејл адреса на iastate.edu
Zhong-Hua HanDepartment of Fluid Mechanics, School of Aeronautics, Northwestern Polytechnical UniveristyВерификована је имејл адреса на nwpu.edu.cn
Chao HuAssociate Professor at University of ConnecticutВерификована је имејл адреса на uconn.edu

Прати

Xiaosong Du

Assistant Professor, Mechanical and Aerospace Engineering, Missouri S&T

Верификована је имејл адреса на mst.edu - Почетна страница

Artificial Intelligence Multidisciplinary Design Optimization Surrogate Modeling Generative AI


Наслов Сортирај по наводима Сортирај по години Сортирај по наслову	Навело Навело	Година
Machine learning in aerodynamic shape optimization J Li, X Du, JRRA Martins Progress in Aerospace Sciences 134, 100849, 2022	236	2022
Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling X Du, P He, JRRA Martins Aerospace Science and Technology 113, 106701, 2021	164	2021
A B-spline-based generative adversarial network model for fast interactive airfoil aerodynamic optimization X Du, P He, JRRA Martins AIAA scitech 2020 forum, 2128, 2020	73	2020
Efficient yield estimation of multiband patch antennas by polynomial chaos‐based Kriging L Leifsson, X Du, S Koziel International Journal of Numerical Modelling: Electronic Networks, Devices …, 2020	41	2020
Aerodynamic inverse design using multifidelity models and manifold mapping X Du, J Ren, L Leifsson Aerospace science and technology 85, 371-385, 2019	40	2019
Application of multifidelity optimization techniques to benchmark aerodynamic design problems J Ren, AS Thelen, A Amrit, X Du, LT Leifsson, Y Tesfahunegn, S Koziel 54th AIAA aerospace sciences meeting, 1542, 2016	34	2016
Optimum aerodynamic shape design under uncertainty by utility theory and metamodeling X Du, L Leifsson Aerospace Science and Technology 95, 105464, 2019	31	2019
Learning high-dimensional parametric maps via reduced basis adaptive residual networks T O’Leary-Roseberry, X Du, A Chaudhuri, JRRA Martins, K Willcox, ... Computer Methods in Applied Mechanics and Engineering 402, 115730, 2022	28	2022
Efficient model-assisted probability of detection and sensitivity analysis for ultrasonic testing simulations using stochastic metamodeling X Du, L Leifsson, W Meeker, P Gurrala, J Song, R Roberts Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …, 2019	23	2019
Single-and multipoint aerodynamic shape optimization using multifidelity models and manifold mapping J Nagawkar, J Ren, X Du, L Leifsson, S Koziel Journal of Aircraft 58 (3), 591-608, 2021	20	2021
Multifidelity modeling by polynomial chaos-based cokriging to enable efficient model-based reliability analysis of NDT systems X Du, L Leifsson Journal of Nondestructive Evaluation 39 (1), 13, 2020	18	2020
Multifidelity model-assisted probability of detection via Cokriging X Du, L Leifsson NDT & E International 108, 102156, 2019	17	2019
Efficient uncertainty propagation for MAPOD via polynomial chaos-based Kriging X Du, L Leifsson Engineering Computations 37 (1), 73-92, 2020	14	2020
Applications of polynomial chaos-based cokriging to aerodynamic design optimization benchmark problems J Nagawkar, LT Leifsson, X Du AIAA Scitech 2020 Forum, 0542, 2020	11	2020
Novel adaptive sampling algorithm for POD-based non-intrusive reduced order model J Wang, X Du, JR Martins AIAA Aviation 2021 Forum, 3051, 2021	9	2021
Aerodynamic design of a rectangular wing in subsonic inviscid flow by direct and surrogate-based optimization X Du, A Amrit, AS Thelen, LT Leifsson, Y Zhang, ZH Han, S Koziel 35th AIAA Applied Aerodynamics Conference, 4366, 2017	9	2017
Adaptive projected residual networks for learning parametric maps from sparse data T O’Leary-Roseberry, X Du, A Chaudhuri, JR Martins, K Willcox, ... arXiv preprint arXiv:2112.07096, 2021	7	2021
Airfoil design under uncertainty using non-intrusive polynomial chaos theory and utility functions X Du, L Leifsson, S Koziel, A Bekasiewicz Procedia Computer Science 108, 1493-1499, 2017	7	2017
Super resolution generative adversarial networks for multi-fidelity pressure distribution prediction X Du, JR Martins AIAA SCITECH 2023 Forum, 0533, 2023	6	2023
Aerodynamic design of the RAE 2822 in transonic viscous flow: Single-and multi-point optimization studies A Amrit, X Du, AS Thelen, LT Leifsson, S Koziel 35th AIAA Applied Aerodynamics Conference, 3751, 2017	6	2017

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