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of 67
pro vyhledávání: '"Zhong, Weiheng"'
Since the shape of industrial endoscopes is passively altered according to the contact around it, manual inspection approaches of aeroengines through the inspection ports have unreachable areas, and it's difficult to traverse multistage blades and in
Externí odkaz:
http://arxiv.org/abs/2412.03508
Autor:
Mao, Weixin, Zhong, Weiheng, Jiang, Zhou, Fang, Dong, Zhang, Zhongyue, Lan, Zihan, Jia, Fan, Wang, Tiancai, Fan, Haoqiang, Yoshie, Osamu
Existing policy learning methods predominantly adopt the task-centric paradigm, necessitating the collection of task data in an end-to-end manner. Consequently, the learned policy tends to fail to tackle novel tasks. Moreover, it is hard to localize
Externí odkaz:
http://arxiv.org/abs/2412.00171
Autor:
Zhong, Weiheng, Meidani, Hadi
Engineering design problems often involve solving parametric Partial Differential Equations (PDEs) under variable PDE parameters and domain geometry. Recently, neural operators have shown promise in learning PDE operators and quickly predicting the P
Externí odkaz:
http://arxiv.org/abs/2408.01600
Autor:
Zhong, Weiheng, Meidani, Hadi
Solving parametric Partial Differential Equations (PDEs) for a broad range of parameters is a critical challenge in scientific computing. To this end, neural operators, which \textcolor{black}{predicts the PDE solution with variable PDE parameter inp
Externí odkaz:
http://arxiv.org/abs/2404.13646
Traffic forecasting is an important issue in intelligent traffic systems (ITS). Graph neural networks (GNNs) are effective deep learning models to capture the complex spatio-temporal dependency of traffic data, achieving ideal prediction performance.
Externí odkaz:
http://arxiv.org/abs/2305.00985
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans mult
Externí odkaz:
http://arxiv.org/abs/2209.13123
Autor:
Zhong, Weiheng, Meidani, Hadi
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 February 2025 434
Autor:
Zhong, Weiheng, Meidani, Hadi
We propose a new class of physics-informed neural networks, called physics-informed Variational Autoencoder (PI-VAE), to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In these problems the governing equations are
Externí odkaz:
http://arxiv.org/abs/2203.11363
Publikováno v:
In Journal of Colloid And Interface Science 15 July 2024 666:221-231
Autor:
Zhong, Weiheng, Meidani, Hadi
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2023 403 Part A