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Grid residual adaptive resampling for physics-informed neural networks to solve incompressible Navier-Stokes equations

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Journal of Applied Physics 2025年第15期137卷

作者： Gao, Lei Zhang, Dan Chen, Yaoran Tu, Yihui Zhang, Xiangyu Li, Xiaowei School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Institute of Artificial Intelligence Collaborative Innovation Center for the Marine Artificial Intelligence Shanghai University Shanghai200444 China Engineering Research Center of Unmanned Intelligent Marine Equipment Ministry of Education Shanghai200444 China School of Future Technology Shanghai University Shanghai200444 China Department of Mathematics Shanghai University Shanghai200444 China Newtouch Center for Mathematics of Shanghai University Shanghai University Shanghai200444 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

This study aims to tackle the challenges of nonlinearity and coupling that Physics-Informed Neural Networks (PINNs) encounter when solving the incompressible Navier-Stokes equations. The research core is centered around optimizing the sampling strategy of residual collocation points to enhance PINNs’ capability of capturing the key features of the Navier-Stokes solutions. To this end, we propose a Grid Residual Adaptive Resampling (GRAR) strategy to improve the accuracy of PINNs in solving the Navier-Stokes equations. The GRAR-PINN method selectively samples residual collocation points based on grid residual values, allowing it to more effectively capture the essential characteristics of the Navier-Stokes solutions. To validate the effectiveness of this approach, we carried out numerical experiments using two-dimensional steady Kovasznay flow and two-dimensional triangular cylinder flow. The results show that the GRAR-PINN method can significantly enhance the accuracy of PINNs in solving the Navier-Stokes equations with relatively fewer residual collocation points through continuous resampling. This method not only alleviates the problem of repeatedly adjusting the number of residual collocation points but also addresses the challenge of capturing the key features of the Navier-Stokes solutions using PINNs. © 2025 Author(s).

关键词： Navier Stokes equations

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VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction

arXiv

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arXiv 2025年

作者： Chen, Zizhi Han, Minghao Zhang, Xukun Ma, Shuwei Liu, Tao Wei, Xing Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China

Multimodal learning combining pathology images and genomic sequences enhances cancer survival analysis but faces clinical implementation barriers due to limited access to genomic sequencing in under-resourced regions. To enable survival prediction using only whole-slide images (WSI), we propose the Visual-Genomic Answering-Guided Transformer (VGAT), a framework integrating Visual Question Answering (VQA) techniques for genomic modality reconstruction. By adapting VQA’s text feature extraction approach, we derive stable genomic representations that circumvent dimensionality challenges in raw genomic data. Simultaneously, a cluster-based visual prompt module selectively enhances discriminative WSI patches, addressing noise from unfiltered image regions. Evaluated across five TCGA datasets, VGAT outperforms existing WSI-only methods, demonstrating the viability of genomic-informed inference without sequencing. This approach bridges multimodal research and clinical feasibility in resource-constrained settings. The code link is https://***/CZZZZZZZZZZZZZZZZZ/VGAT. Copyright © 2025, The Authors. All rights reserved.

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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Vector Field-Guided Circumnavigation for an Unmanned Surface Vehicle With Boundary and Obstacle Constraints

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Wu, Nailong Wang, Jigang Wang, Yueying He, Shuping Donghua University College of Information Science and Technology Shanghai201620 China Donghua University Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai201620 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

This article aims to tackle the challenge of rapid target circumnavigation by unmanned surface vehicles (USVs) under boundary and obstacle constraints through leveraging an emerging computing architecture specifically tailored for unmanned systems. Owing to the underactuation of USV dynamic systems and the presence of various time-varying surface disturbances, attaining rapid and accurate position control along with maneuverability poses a significant challenge to USVs. Therefore, when an USV maneuvers around a target in a confined search area with obstacles, it is essential for the guidance law to consider collision risks and environmental disturbances. This paper proposes a modified Lyapunov vector field with heading deviation compensation to circle the target in complex search regions. The vector field is designed to increase the radial velocity of the USV and accelerate convergence towards the target circle during the pursuit phase. By combining the proposed Lyapunov vector field with a boundary vector field, the USV can swiftly avoid boundaries and dynamic obstacles and then continue circling the target. The proposed computing architecture extends the application scope of the target circumnavigation to practical water surface environments, which is validated by extensive simulations and rigorous experiments. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Evolution of the polarization state and OAM-resolved polarization of full Poincaré beams propagating in the atmosphere

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Optics Express 2025年第9期33卷 19067-19079页

作者： Wang, Yakun Liu, Lin Chen, Yahong Wang, Fei Wu, Dan Cai, Yangjian Liu, Yonglei School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou215006 China School of Optical and Electronic Information Suzhou City University Suzhou215104 China Suzhou Key Laboratory of Intelligent Photoelectric Perception Soochow University Suzhou215006 China Shandong Provincial Engineering and Technical Center of Light Manipulation Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and Electronics Shandong Normal University Jinan250014 China Joint Research Center of Light Manipulation Science and Photonic Integrated Chip East China Normal University Shandong Normal University East China Normal University Shanghai200241 China

A detailed study is conducted on the polarization characteristics of full Poincaré beams (FPBs) propagating through atmospheric turbulence, including polarization state, degree of polarization, OAM-resolved Stokes parameters, and OAM-resolved degree of polarization. Analytical solutions for the cross-spectral density matrix and its corresponding coherence-orbital angular momentum (COAM) matrix are formulated. We show that the polarization distribution of the propagating FPBs in turbulence, at any transversal plane, still remains a stereographic projection of the Poincaré sphere but rotates with respect to its propagation axis. The rotation angle, however, is independent of the turbulence. Further, we reveal the selection rule for governing the correlations of any two OAM modes which contain the turbulence-disturbed FPBs through OAM mode analysis. Based on the selection rule, we show that the OAM-resolved Stokes parameters and OAM-resolved degree of polarization exhibit diverse evolution behavior in the turbulence. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Spectral density

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TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining

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Computer Methods and Programs in Biomedicine 2025年 267卷 108809-108809页

作者： Zhang, Qing Wu, Xiaoxiao Li, Xiang Ma, Wei Wu, Tongquan Li, Liuyue Hu, Fan Xie, Yicheng Wu, Xinglong School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Hubei Wuhan430205 China School of Mechanical Engineering Hefei University of Technology Hefei China Faculty of Applied Science and Engineering University of Toronto Canada UNISKIN Research Institute on Skin Aging Inertia Shanghai Biotechnology Co. Ltd Shanghai China DermaHealth Shanghai Biotechnology Co. Ltd. Shanghai China Department of Dermatology Children's Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Zhejiang Province Hangzhou310052 China

Background: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, conventional supervised or semi-supervised methodologies are contingent on expert labels and incur substantial labeling costs, In contrast self-supervised pre-training strategies leverage unlabeled data during the pre-training phase and utilise a limited amount of labeled data in the fine-tuning phase, thereby greatly reducing labor costs. Furthermore, the fine-tuning does not need to learn the feature representations from scratch, enhancing the efficiency and transferability of the model. Methods: The proposed methodology is outlined below. The deep learning framework, TransAnno-Net, is based on transfer learning and a Transformer architecture. It has been designed for efficient and accurate cell type annotations in large-scale scRNA-seq datasets of mouse lung organs. Specifically, TransAnno-Net is pre-trained on the scRNA-seq lung data of approximately 100,000 cells to acquire gene-gene similarities via self-supervised learning. It is then migrated to a relatively small number of datasets to fine-tune specific cell type annotation tasks. To address the issue of imbalance in cell types commonly observed in scRNA-seq data, we applied a random oversampling technique is applied to the fine-tuned dataset. This is done to mitigate the impact of distributional imbalance on the annotation outcomes. Results: The experimental findings demonstrate that TransAnno-Net exhibits superior performance with an AUC of 0.979, 0.901, and 0.982, respectively, on three mouse lung datasets, outperforming eight state-of-the-art (SOTA) methods. In addition, TransAnno-Net demonstrates robust performance on cross-organ, cross-platform datasets, and is competitive with the fully supervised learning-based method. Conclusion: The TransAnno-Net method

关键词： Self-supervised learning

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Influence of laser powder bed fusion of high-entropy alloy transition layer on the wetting and spreading behaviour of Al alloy on steel substrate surface

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Journal of Materials Processing technology 2025年 340卷

作者： Zeng, Yijian Yang, Jin Dou, Tianyu Zheng, Min Zhao, Yixuan Oliveira, J.P. Shen, Jiajia Tan, Caiwang Xia, Hongbo Zhang, Hua School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai201620 China School of Materials Science and Engineering Nanchang Hangkong University Nanchang330063 China CENIMAT|I3N Department of Materials Science School of Science and Technology NOVA University Lisbon Caparica Portugal State Key Laboratory of Precision Welding & Joining of Materials and Structures Harbin Institute of Technology Harbin150001 China Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components Shanghai201620 China

The high-quality joining of Al alloys to steel is widely used in automotive manufacturing. However, because of the significant differences in their thermophysical properties, the wetting of Al alloys on steel is very limited, which affects the joining quality. To address this challenge, the laser powder bed fusion (LPBF) technique was used to prepare an FeCoNiCrMn high entropy alloy (HEA) with a dense transition layer (DTL) and micropillar transition layer (MTL) on a steel substrate for the first time. Then, the dynamic wetting and spreading behaviours of Al-12Si alloy over the transition layer-free, DTL, and MTL steels were comparatively studied. The spreading mechanisms were revealed by the spreading dynamics and an analysis of the interfacial microstructure. This study investigated a new method to improve the wetting of Al alloys on steel substrates and elucidated the mechanism of the effect of the HEA transition layer formed using LPBF on Al/steel wetting, which may provide some guidance for improving brazing, welding, coating, and other processes involving solid/liquid interfacial interactions. © 2025

关键词： Aluminum alloys

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Hierarchy-Boosted Funnel Learning for Identifying Semiconductors with Ultralow Lattice Thermal Conductivity

arXiv

引用

arXiv 2025年

作者： Wu, Mengfan Yan, Shenshen Ren, Jie Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Center for Phononics and Thermal Energy Science Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China

Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity (κL). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow κL, thereby circumventing large-scale brute-force ab initio calculations without clear objectives. As a result, we provide a list of candidates with ultralow κL for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials. © 2025, CC BY-NC-ND.

关键词： Structural properties

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Bolt Looseness Quantitative Visual Detection With Cross-Modal Fusion

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Structural Control and Health Monitoring 2025年第1期2025卷

作者： Wang, Zhipeng Ma, Jiajun Xue, Gui Gu, Feida Ren, Ruochen Zhou, Yanmin He, Bin College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201109 China State Key Laboratory of Intelligent Autonomous Systems Shanghai201109 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201210 China

intelligent bolt looseness detection systems offer significant potential for accurately promptly detecting bolt looseness. Bolt looseness detection in high-speed train undercarriages is challenging due to the low-texture surfaces of structural parts and variations of illumination and viewpoint in typical maintenance scenes. These factors hinder the quantification detection of bolt looseness using traditional 2D visual inspection methods. In this paper, we present a cross-modal fusion-based method for the quantification detection of bolt looseness in high-speed train undercarriages. We propose a cross-modal fusion approach using a cross-modal transformer, which integrates 2D images and 3D point clouds to improve adaptability to varying illumination conditions in maintenance scenes. To address geometric projection distortions caused by varying-view perspective transformations, we use the height difference between the bolt cap and the fastening plane in point clouds as the criterion for bolt loosening. The experimental results indicate that the proposed method outperforms the base-line on our dataset of 5823 annotated RGB-D images from a locomotive depot, achieving an average measurement error of 0.39 mm. Copyright © 2025 Zhipeng Wang et al. Structural Control and Health Monitoring published by John Wiley & Sons Ltd.

关键词： Image fusion

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