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Accelerating fluid simulations with graph convolution network predicted flow fields

Aerospace Science and Technology 2025年 164卷

作者： Wei Xian Lim Loh Sher En Jessica Yu Lv Adams Wai-Kin Kong Wai Lee Chan Rolls-Royce@NTU Corporate Lab Nanyang Technological University Singapore 637460 Singapore College of Computing and Data Science Nanyang Technological University Singapore 639798 Singapore State Key Laboratory of Nonlinear Mechanics Institute of Mechanics Chinese Academy of Sciences Beijing 100190 China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore 639798 Singapore

The field of computational fluid dynamics (CFD) is integral to engineering disciplines, particularly for designing systems that operate under complex fluid flow conditions. Accurate simulation of flow fields is essential for optimizing performance across a variety of applications, including aviation, automotive, marine, and renewable energy sectors. Recent advancements in deep learning, particularly graph convolution networks (GCNs), offer promising alternatives for improving simulation processes. This work introduces a novel approach to accelerating fluid simulations using GCNs for flow field initialization. To this end, two different GCN models were employed, incorporating prior knowledge of the problem like its boundary conditions, as well as residual training. Extensive experiments using over 2000 sets of simulation results of various NACA airfoil shapes and flow conditions demonstrate that GCN-based initialization significantly reduces computational resources while maintaining high accuracy, achieving a 30% – 50% reduction in simulation time compared to conventional CFD initialization method.

关键词： Deep learning Graph convolution networks Reynolds-averaged Navier–Stokes simulations Flow initialization

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XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

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Nature Communications 2023年第1期14卷 6870页

作者： Cai, Mingxuan Wang, Zhiwei Xiao, Jiashun Hu, Xianghong Chen, Gang Yang, Can Department of Biostatistics City University of Hong Kong Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou 511458 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen 518172 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China WeGene Shenzhen Zaozhidao Technology Co. Ltd Shenzhen 518040 China Graduate Affairs Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. © 2023, The Author(s).

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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3D printed optical fibre preforms from silica contained resin 6

3D printed optical fibre preforms from silica contained resi...

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6th International Workshop on Specialty Optical Fibers and Their Applications, WSOF 2019

作者： Chu, Yushi Canning, John Fu, Xinghu Luo, Yanhua Cook, Kevin Zhang, Jianzhong Peng, Gang-Ding Tech Lab School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 & 2019 Australia Photonics and Optical Communications School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Lab of In-Fiber Integrated Optics of Ministry of Education School of Science Harbin Engineering University Harbin150001 China

3D printed glass optical fiber preforms have been fabricated from silica containing resin. The fabrication process includes resin preparation, preform printing, debinding and sintering. Results demonstrate the silica ... 详细信息

ISBN: (纸本)9781510631618

关键词： Sintering

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

arXiv

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arXiv 2020年

作者： Wang, Weisen Li, Xirong Xu, Zhiyan Yu, Weihong Zhao, Jianchun Ding, Dayong Chen, Youxin The MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China Aimc Lab School of Information Renmin University of China Beijing100872 China Vistel Ai Lab Visionary Intelligence Ltd. Beijing100872 China The Key Lab of Ocular Fundus Disease Chinese Academy of Medical Sciences Dept. of Ophthalmology Peking Union Medical College Hospital Beijing100730 China

This paper tackles automated categorization of Agerelated Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multimodal AMD categorization. Code and data are available at https://***/li-xirong/mmc-amd. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

引用

3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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Cryptanalysis of a Pairing-Based Anonymous key Agreement Scheme for Smart Grid 15th

Cryptanalysis of a Pairing-Based Anonymous Key Agreement Sch...

引用

15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Liang, Xiao-Cong Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Yeh, Jyh-Haw Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science Boise State University Idaho United States

ISBN: (纸本)9789811397134

Authentication and key agreement schemes in smart grid have received much attentions by researchers in recent years. Recently, Mahmood et al. proposed a secure pairing-based anonymous key exchange scheme in smart grid. However, we demonstrate that their scheme is insecure against an ephemeral secret leakage attack and an impersonation attack according to the Canetti-Krawczyk adversarial model in this paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Smart power grids

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SLBCNN: A Improved Deep Learning Model for Few-Shot Charge Prediction

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Procedia Computer Science 2020年 174卷 32-39页

作者： Xue Li Yuan Rao Wanzhen Wang Cong Feng Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Precessiong School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiaotong University Xi’an China

Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A major challenge in developing an efficient few-shot charge prediction algorithm is how to effectively use all available features contained in the typical cases. To this end, we proposed a novel few-shot charge prediction method based on deep learning framework, named Semi-supervised Learning with BERT-text-Convolutional neural network (i.e. SLBCNN), which utilizes the Attribute-Tri-Training method to extract the features from the few-shot case data. Experiments on three datasets of Criminal-S, Criminal-M, and Criminal-L demonstrate that SLBCNN achieves the state-of-the-art performance, boosting the F1 by 5.2%, 4.2%, and 1.5%, respectively.

关键词： few-shot charge prediction discriminative legal attributes Semi-Supervised Learning

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Deep keyphrase completion

arXiv

引用

arXiv 2021年

作者： Zhao, Yu Song, Jia Feng, Huali Zhuang, Fuzhen Li, Qing Wang, Xiaojie Liu, Ji Fintech Innovation Center Financial Intelligence and Financial Engineering Key Laboratory Southwestern University of Finance and Economics Chengdu China Institute of Artificial Intelligence Beihang University Beijing100191 China Xiamen Data Intelligence Academy of ICT CAS China School of Artifical Intelligence Beijing University of Posts and Telecommunications China AI platform and Seattle AI Lab Kwai Inc

keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies have been made for keyphrase completion (KPC). KPC aims to generate more keyphrases for document (e.g. scientific publication) taking advantage of document content along with a very limited number of known keyphrases, which can be applied to improve text indexing system, etc. In this paper, we propose a novel KPC method with an encoder-decoder framework. We name it deep keyphrase completion (DKPC) since it attempts to capture the deep semantic meaning of the document content together with known keyphrases via a deep learning framework. Specifically, the encoder and the decoder in DKPC play different roles to make full use of the known keyphrases. The former considers the keyphrase-guiding factors, which aggregates information of known keyphrases into context. On the contrary, the latter considers the keyphrase-inhibited factor to inhibit semantically repeated keyphrase generation. Extensive experiments on benchmark datasets demonstrate the efficacy of our proposed model. © 2021, CC BY.

关键词： Signal encoding

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