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Isogeometric Convolution Hierarchical Deep-learning Neural Network: Isogeometric analysis with versatile adaptivity 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2023, 卷号: 417, 页码: 46
Authors:  Zhang L(张磊);  Park, Chanwook;  Lu, Ye;  Li, Hengyang;  Mojumder, Satyajit;  Saha, Sourav;  Guo, Jiachen;  Li, Yangfan;  Abbott, Trevor;  Wagner, Gregory J.;  Tang, Shaoqiang;  Liu, Wing Kam
Adobe PDF(9021Kb)  |  Favorite  |  View/Download：88/5  |  Submit date：2024/01/08
Convolution isogeometric analysis (C-IGA)  Convolution hierarchical deep-learning neural network (C-hiDeNN)  Software 2.0  r-h-p-s-a adaptive finite element method (FEM)  High-order smoothness and convergence  
Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime 期刊论文
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 卷号: 172, 页码: 107645
Authors:  Jia, Yinfeng;  Fu, Rui;  Ling, Chao;  Shen, Zheng;  Zheng, Liang;  Zhong, Zheng;  Hong YS(洪友士)
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Fatigue life prediction  Deep learning method  Laser powder bed fusion  Ti-6Al-4V  Very -high -cycle fatigue  
基于模型与数据驱动相结合的嵌入式大气数据系统算法研究 学位论文
博士论文，北京: 中国科学院大学, 2023
Authors:  刘洋
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大气数据系统，嵌入式大气数据系统，模型驱动，数据驱动，神经网络  
A novel defect based fatigue damage model coupled with an optimized neural network for high cycle fatigue analysis of casting alloys with surface defect 期刊论文
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 卷号: 170, 页码: 107538
Authors:  Gao, Tongzhou;  Ji, Chenhao;  Zhan, Zhixin;  Huang, Yingying;  Liu CQ(刘传奇);  Hu, Weiping;  Meng, Qingchun
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High cycle fatigue  Casting alloys  Surface defect  Damage model  Optimized neural network  
高速磁浮列车悬浮间隙仿真预测 期刊论文
同济大学学报(自然科学版), 2023, 卷号: 51, 期号: 03, 页码: 351-359
Authors:  吴晗;  刘梦娟;  曾晓辉
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高速磁浮列车  长短时记忆(LSTM)神经网络  数值仿真  动力响应预测  
A computational method for the load spectra of large-scale structures with a data-driven learning algorithm 期刊论文
SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2023, 卷号: 66, 期号: 1, 页码: 141-154
Authors:  Chen XJ(陈贤佳);  Yuan, Zheng;  Li, Qiang;  Sun, ShouGuang;  Wei YJ(魏宇杰)
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load spectrum  computational mechanics  deep learning  data-driven modeling  gated recurrent unit neural network  
Estimating forces from cross-sectional data in the wake of flows past a plate using theoretical and data-driven models 期刊论文
PHYSICS OF FLUIDS, 2022, 卷号: 34, 期号: 11, 页码: 19
Authors:  Tong, Wenwen;  Wang SZ(王士召);  Yang, Yue
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Control of quasi-equilibrium state of annular flow through reinforcement learning 期刊论文
PHYSICS OF FLUIDS, 2022, 卷号: 34, 期号: 9, 页码: 94105
Authors:  Chen Y(陈一);  Duan L(段俐);  Kang Q(康琦)
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Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence 期刊论文
Journal of Fluid Mechanics, 2022, 卷号: 932, 页码: A23
Authors:  Yu ZP(于长平);  Yuan ZL;  Qi H(齐涵);  Wang JC;  Li XL(李新亮);  Chen SY
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A Direct-Forcing Immersed Boundary Method for Incompressible Flows Based on Physics-Informed Neural Network 期刊论文
Fluids, 2022, 卷号: 7, 期号: 2, 页码: 56
Authors:  Huang Y(黄毅);  Zhang ZY(张治愚);  Zhang X(张星)
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physics-informed neural networks (PINN)  direct-forcing immersed boundary method  incompressible laminar flow  circular cylinder