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Learning transferable cross-modality representations for few-shot hyperspectral and LiDAR collaborative classification 期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 卷号: 126, 页码: 11
作者:  Dai, Mofan;  Xing, Shuai;  Xu, Qing;  Wang, Hanyun;  Li, Pengcheng;  Sun, Yifan;  Pan, Jiechen;  Li YQ(李玉琼)
收藏  |  浏览/下载：29/0  |  提交时间：2024/02/26
Multimodal remote sensing data  Meta-learning  Few-shot learning  Cross-modality feature learning  
Stochastic dynamics of aircraft ground taxiing via improved physics-informed neural networks 期刊论文
NONLINEAR DYNAMICS, 2024, 页码: 16
作者:  Zhang, Ying;  Jin, Zhengrong;  Wang L(王笼);  Zheng, Kaixin;  Jia, Wantao
收藏  |  浏览/下载：28/0  |  提交时间：2024/02/19
PINNs  Aircraft ground taxiing model  Fokker-Planck equations  Inverse problem  
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
作者:  Zhang, Lei;  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)  |  收藏  |  浏览/下载：61/2  |  提交时间：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 (vol 172, 107645, 2023) 期刊论文
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 卷号: 176, 页码: 1
作者:  Jia, Yinfeng;  Fu, Rui;  Ling, Chao;  Shen, Zheng;  Zheng, Liang;  Zhong, Zheng;  Hong YS(洪友士)
Adobe PDF(236Kb)  |  收藏  |  浏览/下载：90/3  |  提交时间：2023/12/11
DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 卷号: 491, 页码: 112344
作者:  Lv JQ(吕家琦);  Hong QZ(洪启臻);  Wang XY(王小永);  Mao, Zhiping;  Sun QH(孙泉华)
Adobe PDF(5558Kb)  |  收藏  |  浏览/下载：64/0  |  提交时间：2023/09/26
Hypersonic  Thermochemical nonequilibrium  State-to-state approach  Deep learning  Multiphysics  Data assimilation  
Predicting continuum breakdown with deep neural networks 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 卷号: 489, 页码: 112278
作者:  Xiao TB(肖天白);  Schotthoefer, Steffen;  Frank, Martin
Adobe PDF(3004Kb)  |  收藏  |  浏览/下载：29/0  |  提交时间：2023/09/05
Computational fluid dynamics  Kinetic theory  Boltzmann equation  Multi-scale method  Deep learning  
Distinguishing and Matching-Aware Unsupervised Point Cloud Completion 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 9, 页码: 5160-5173
作者:  Xiao, Haihong;  Li YQ(李玉琼);  Kang, Wenxiong;  Wu, Qiuxia
Adobe PDF(3422Kb)  |  收藏  |  浏览/下载：53/0  |  提交时间：2023/10/23
Deep learning  point cloud completion  3D vision  
Force measurement using strain-gauge balance in shock tunnel based on deep learning 期刊论文
CHINESE JOURNAL OF AERONAUTICS, 2023, 卷号: 36, 期号: 8, 页码: 43-53
作者:  Nie SJ(聂少军);  Wang YP(汪运鹏);  Jiang ZL(姜宗林)
Adobe PDF(2346Kb)  |  收藏  |  浏览/下载：21/2  |  提交时间：2024/02/26
Convolutional neural net-works  Deep learning  Frequency domain analysis  Force measurement  Time domain analysis  Recurrent neural networks  
Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method 期刊论文
SENSORS, 2023, 卷号: 23, 期号: 13, 页码: 6030
作者:  Yang ZL(杨智岚);  Zhang HY(张昊越);  Xu P(徐鹏);  Luo ZR(罗子人)
Adobe PDF(3374Kb)  |  收藏  |  浏览/下载：26/0  |  提交时间：2023/09/05
Noise2Noise  deep learning  denoising  accelerometer  inertial sensor  
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
作者:  Jia, Yinfeng;  Fu, Rui;  Ling, Chao;  Shen, Zheng;  Zheng, Liang;  Zhong, Zheng;  Hong YS(洪友士)
Adobe PDF(7230Kb)  |  收藏  |  浏览/下载：56/0  |  提交时间：2023/06/15
Fatigue life prediction  Deep learning method  Laser powder bed fusion  Ti-6Al-4V  Very -high -cycle fatigue