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pro vyhledávání: '"Wang Hongrui"'
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations. Specifically, Euler-Bernoulli and Timoshenko beam models on the Winkler foundation are simulated using a transfer learning approach within a causal
Externí odkaz:
http://arxiv.org/abs/2311.00578
Autor:
Kapoor, Taniya, Chandra, Abhishek, Tartakovsky, Daniel M., Wang, Hongrui, Nunez, Alfredo, Dollevoet, Rolf
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance
Externí odkaz:
http://arxiv.org/abs/2308.08989
Autor:
Cai, Zijian, Tan, Zhaoxuan, Lei, Zhenyu, Zhu, Zifeng, Wang, Hongrui, Zheng, Qinghua, Luo, Minnan
As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-
Externí odkaz:
http://arxiv.org/abs/2306.17408
Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity of social platforms. Though existing graph-based Twitter bot detection
Externí odkaz:
http://arxiv.org/abs/2306.12870
Autor:
Wang Hongrui, Wei Ran
Publikováno v:
E3S Web of Conferences, Vol 194, p 01006 (2020)
With the development of computer technology and Internet technology, more and more energy saving monitoring and management platform systems have been established. The energy saving monitoring and management platform has incomparable advantages in aut
Externí odkaz:
https://doaj.org/article/b637f7ed18584e2c81219fea964fe1c3
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to pre
Externí odkaz:
http://arxiv.org/abs/2304.00369
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2023
This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theory, where the double beams are connected wit
Externí odkaz:
http://arxiv.org/abs/2303.01055
Autor:
Feng, Xinshun, Wan, Herun, Feng, Shangbin, Wang, Hongrui, Zhou, Jun, Zheng, Qinghua, Luo, Minnan
Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers. Many methods have been put forward to tackle this problem in recent y
Externí odkaz:
http://arxiv.org/abs/2208.09027
Autor:
Feng, Shangbin, Tan, Zhaoxuan, Wan, Herun, Wang, Ningnan, Chen, Zilong, Zhang, Binchi, Zheng, Qinghua, Zhang, Wenqian, Lei, Zhenyu, Yang, Shujie, Feng, Xinshun, Zhang, Qingyue, Wang, Hongrui, Liu, Yuhan, Bai, Yuyang, Wang, Heng, Cai, Zijian, Wang, Yanbo, Zheng, Lijing, Ma, Zihan, Li, Jundong, Luo, Minnan
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph stru
Externí odkaz:
http://arxiv.org/abs/2206.04564
Publikováno v:
In Optics Communications 15 August 2024 565