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of 405
pro vyhledávání: '"Lv Weifeng"'
Experimental evaluation of velocity sensitivity for conglomerate reservoir rock in Karamay oil field
Autor:
Han Haishui, Zhang Qun, Lv Weifeng, Han Lu, Ji Zemin, Zhang Shanyan, Zhao Changhong, Kang Hao, Sun Linghui, Shen Rui
Publikováno v:
Science and Engineering of Composite Materials, Vol 30, Iss 1, Pp 24-32 (2023)
Velocity sensitivity refers to the possibility and degree of reservoir permeability decline caused by the migration of various particles in the reservoir rock due to the increase in fluid flow velocity and the blockage of pore channels. To improve th
Externí odkaz:
https://doaj.org/article/41ca31b04ec0476c89403ff3168e5459
Temporal link prediction, aiming at predicting future interactions among entities based on historical interactions, is crucial for a series of real-world applications. Although previous methods have demonstrated the importance of relative encodings f
Externí odkaz:
http://arxiv.org/abs/2410.04013
User profiling and region analysis are two tasks of significant commercial value. However, in practical applications, modeling different features typically involves four main steps: data preparation, data processing, model establishment, evaluation,
Externí odkaz:
http://arxiv.org/abs/2311.10471
Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fide
Externí odkaz:
http://arxiv.org/abs/2305.12347
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explor
Externí odkaz:
http://arxiv.org/abs/2303.13047
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular g
Externí odkaz:
http://arxiv.org/abs/2301.00427
Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permu
Externí odkaz:
http://arxiv.org/abs/2212.01842
Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand prediction is a valuable but challenging problem due to several factor
Externí odkaz:
http://arxiv.org/abs/2206.15005
Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known
Externí odkaz:
http://arxiv.org/abs/2205.15653
Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly focus on the mode
Externí odkaz:
http://arxiv.org/abs/2204.05490