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[11] Zhengxiao Ma,Ruoye Xiao.Data-driven reduced order modeling for parametrized time-dependent flow problems.[期刊]:Physics of Fluids,2022,34
[12] Ma Wenjun,Jun Zhang.Non-intrusive reduced order modeling for flowfield reconstruction based on residual neural network.[期刊]:Acta Astronautica,183:346-362
[13] .航空航天CFD物理模型和计算方法的述评与挑战.[期刊]:􏰝 空气动力学学报,2020,38(5):829-857
[14] .A study of several artificial viscosity models within the discontinuous Galerkin framework.[期刊]:Communications in Computational Physics,2020,27(5):1309-1343
[15] .Non-intrusive reduced-order modeling for fluid problems: A brief review.[期刊]:Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,2019,233(16):5896–5912
[16] .Adaptive non-intrusive reduced order modeling for compressible flows.[期刊]:Journal of Computational Physics,2019,397:108855
[17] .Flowfield reconstruction method using artificial neural network.[期刊]:AIAA JOURNAL,2019,57(2):482-498
[18] .Effects of artificial viscosity and upwinding on spectral properties of the discontinuous Galerkin method.[期刊]:Computers & Fluids,2018,175:276–292
[19] Jian Yu.Revisit of dilation-based shock capturing for discontinuous Galerkin methods.[期刊]:Applied Mathematics and Mechanics,2018,39(3):379–394
[20] Jian Yu.Suitability of artificial viscosity discontinuous Galerkin method for compressible turbulence.[期刊]:SCIENCE CHINA Technological Sciences,2017,60(7):1032–1049

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