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Machine learning for modelling unstructured grid data in computational physics: A review

Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： Reinforcement learning

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Improving the microenvironment of tracking photovoltaic systems promotes soil organic carbon accumulation by mediating plant carbon inputs and microbial necromass retention

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Journal of environmental management 2025年 388卷 125937页

作者： Siqi Zhang Jirui Gong Rui Wang Kai Zheng Weiyuan Zhang Xuede Dong Yuxia Hu Guisen Yang Chenyi Yan Shangpeng Zhang Ruijing Wang Tong Wang State Key Laboratory of Earth Surface Processes and Resource Ecology MOE Engineering Research Center of Desertification and Blown-sand Control Faculty of Geographical Science Beijing Normal University No. 19 Xinjiekouwai Street Haidian District Beijing 100875 China. State Key Laboratory of Earth Surface Processes and Resource Ecology MOE Engineering Research Center of Desertification and Blown-sand Control Faculty of Geographical Science Beijing Normal University No. 19 Xinjiekouwai Street Haidian District Beijing 100875 China. Electronic address: jrgong@***. CGN New Energy Holdings Co. Ltd. Hanwei International Plaza Fengtai District Beijing 100071 China. Inner Mongolia Ganze Desert Technology Co. Ltd. High-tech industrial park cloud computing big data enterprise headquarters Kangbashi District Ordos 017004 China.

Photovoltaic systems greatly reduce greenhouse gas emissions. However, the microenvironmental changes of photovoltaic modules affect soil organic carbon (SOC) and improve carbon sequestration in terrestrial ecosystems to mitigate global climate change is unclear. We analyzed the effects of different photovoltaic systems on SOC and its fractions in semi-arid grassland, and revealed the SOC formation and stability mechanisms. Tracking photovoltaic systems increased carbon stock, especially proportion of mineral-associated organic carbon (MAOC) in SOC by more than 10 %, and depended on the responses of plant inputs and microbial necromass to microenvironmental changes. Suitable amounts of light and water in the systems improved litter biomass and quality, and promoted MAOC accumulation by increasing the exogenous carbon supply and microbial activity. Such systems also reduced soil carbon loss by reducing heterotrophic respiration caused by low levels of organic acids and recruiting effective microorganisms such as Schizothecium and Lactobacillus. Moreover, changes in litter and root exudates promoted microbial biomass and enzyme activity, mediating the retention of microbial necromass and SOC. The contribution of bacterial necromass carbon to SOC increased by more than 8.5 %. However, fixed photovoltaic systems reduced soil carbon stock by 0.46 kg m due to water limitations that decreased both plant carbon inputs and microbial necromass. Our results revealed the importance of plant inputs and microbial necromass in regulating SOC in photovoltaic systems, and demonstrated that photovoltaic systems can achieve synergies between CO emission reduction and soil carbon sequestration. This provides new insights for formulating carbon management policy and promoting sustainable eco-economic development.

关键词： Litter quantity and quality Microbial necromass Photovoltaic system Root exudates Soil microbes Soil organic carbon

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A secure federated learning framework for 5G networks

arXiv

引用

arXiv 2020年

作者： Liu, Yi Peng, Jialiang Kang, Jiawen Iliyasu, Abdullah M. Niyato, Dusit Abd El-Latif, Ahmed A. School of Data Science and Technology Heilongjiang University Harbin150080 China Energy Research Institute Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom32511 Egypt School of Information Technology and Computer Science Nile University Egypt

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Development of a Predictive Analytic Tool for Detection of Alzheimer's Disease

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： Aw Hui Yee Azian Azamimi Abdullah Juhaida Abu Bakar Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Medical Devices and Life Sciences Cluster Sport Engineering Research Centre Centre of Excellence (SERC) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah.

Alzheimer's disease (AD) is the common neurodegenerative disease that causes the impairment of the brain tissue with a gradual decline in the memory and thinking skills among the elderly population. However, there is a lack of reliable biomarker and effective medical treatment to detect and cure the AD respectively. Since there is a commitment agrees that AD should be the focus on the early diagnosis stage due to the great improvement on the treatment efficacy, hence with the help of imaging modality such as Magnetic Resonance Imaging (MRI), it can provide a detailed brain's features information for analysis and interpretation purposes. Besides that, this project is focusing on the implementation of the supervised machine learning algorithms on the prediction of AD based on the Graphical User Interface (GUI). By referring to the results obtained from classifiers' performance evaluation, Random Forest achieved the best performance in classification of AD with the highest accuracy of 0.7825 and AUC score of 0.8314 along with Lasso Regularization, followed by Gradient Boosting Machine (GBM) with accuracy of 0.7352 and Deep Neural Network (DNN) with accuracy of 0.6201 only. The machine learning with feature selection, Lasso Regularization is performed much better than those without feature selection. Thus, the GUI is then developed to provide convenience for the user to accurately predict their current AD's status.

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Insights, opportunities and challenges provided by large cell atlases

arXiv

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arXiv 2024年

作者： Hemberg, Martin Marini, Federico Ghazanfar, Shila Ajami, Ahmad Al Abassi, Najla Anchang, Benedict Benayoun, Bérénice A. Cao, Yue Chen, Ken Cuesta-Astroz, Yesid DeBruine, Zach Dendrou, Calliope A. De Vlaminck, Iwijn Imkeller, Katharina Korsunsky, Ilya Lederer, Alex R. Meysman, Pieter Miller, Clint Mullan, Kerry Ohler, Uwe Patikas, Nikolaos Schuck, Jonas Siu, Jacqueline H.Y. Triche, Timothy J. Tsankov, Alex van der Laan, Sander W. Yajima, Masanao Yang, Jean Zanini, Fabio Jelic, Ivana The Gene Lay Institute of Immunology and Inflammation Brigham and Women's Hospital Massachusetts General Hospital United States Harvard Medical School BostonMA United States University Medical Center Mainz Mainz Germany Mainz Germany School of Mathematics and Statistics Faculty of Science University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney SydneyNSW2006 Australia Charles Perkins Centre University of Sydney SydneyNSW2006 Australia Goethe University Neurological Institute Edinger Institute University Hospital Frankfurt Frankfurt am Main Germany Goethe University Frankfurt Cancer Institute Frankfurt am Main Germany Frankfurt am Main Germany National Institute of Environmental Health Sciences United States Leonard Davis School of Gerontology University of Southern California Los AngelesCA90089 United States Molecular and Computational Biology Department USC Dornsife College of Letters Arts and Sciences Los AngelesCA90089 United States Biochemistry and Molecular Medicine Department USC Keck School of Medicine Los AngelesCA90089 United States Science Park Hong Kong The University of Texas MD Anderson Cancer Center United States Escuela de Microbiología Universidad de Antioquia Ciudad Universitaria Calle 67 No 12 53-108 Medellín Colombia Instituto Colombiano de Medicina Tropical Universidad CES Sabaneta055413 Colombia School of Computing Grand Valley State University AllendaleMI49401 United States Kennedy Institute of Rheumatology Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford United Kingdom Cornell University United States Brigham and Women’s Hospital Divisions of Genetics and Rheumatology BostonMA United States and Harvard CambridgeMA United States Lausanne1015 Switzerland Adrem data lab Department of Computer Science University of Antwerp Antwerp Belgium The Rector and Visitors The University of Virginia United States M

The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome. © 2024, CC BY-NC-ND.

关键词： Biotic

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Modeling of infant mortality in west sulawesi using zero inflated poisson regression method

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： R Annisa EO Permatasari A T Rumiati Department of Statistics Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java

The number of infant deaths, one of which can be reflected in the Infant Mortality Rate (IMR) is one indicator to determine the development of a country's development. The AKB target in the 2015-2019 National Medium-Term Development Plan is 24 deaths per 1,000 live births. Based on the SDKI 2017, the IMR value in West Sulawesi is 42 deaths per 1,000 live births. Factors suspected of affecting infant mortality in West Sulawesi are not only in the health but also in socio-economic fields. data on the number of infant mortality in West Sulawesi is a data count that contains a value of 0. Therefore, the analysis used is Zero Inflated Poisson (ZIP) Regression to determine what factors influence infant mortality in West Sulawesi. The results of the ZIP regression analysis showed that the best model with the smallest AIC and AICc is 324,3407 and 326,7407 were models with a combination of the variables of the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. The variable that has a significant effect on the log model is the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. In the variable logit model that is influential is the percentage of active maternal and child health services.

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

关键词： Double deep Q-learning Latency and energy efficiency Mobile edge computing Spatio-temporal optimization

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Grouping District / Cities in Papua Based Poverty Indicators

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： R Y Endartyana E O Permatasari Department of Statistics Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java

Welfare of the people is a very important thing in a country. One measure of the welfare of society is seen from indicators of poverty. The government conducts a long-term agreement in terms of encouraging change through SDG's. One of the government's target is less poverty in all its forms by 2030. The area in Indonesia which has the highest percentage of poor people in the provinces of Papua. Hence, the groups based Indicators of Poverty in Papua province using C-Means Clustering and Fuzzy C-Means Clustering. The indicators used in this analysis is an indicator of Health, Education Indicators and Household Characteristics indicator. Based on the analysis results obtained Fuzzy C-Means formed 5 group is the best method to classify District / Cities in Papua Province by minimumicdrate value. Areas that need to be the focus of the government to reduce poverty in Papua is the Puncak District.

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Dynamics of surface electrons in a topological insulator: cyclotron resonance at room temperature

arXiv

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arXiv 2025年

作者： Mohelsky, I. Le Mardelé, F. Dzian, J. Wyzula, J. Sun, X.D. Cho, C.W. Piot, B.A. Shankar, M. Sankar, R. Ferguson, A. Santos-Cottin, D. Maršík, P. Bernhard, C. Akrap, A. Potemski, M. Orlita, M. Laboratoire National des Champs Magnétiques Intenses EMFL CNRS UPR3228 Univ. Grenoble Alpes Univ. Toulouse Univ. Toulouse 3 INSA-T Grenoble France Department of Physics Université de Fribourg Chemin du Musée 3 Fribourg1700 Switzerland Faculty of Mathematics and Physics Charles University Ke Karlovu 5 Prague121 16 Czech Republic Scientific Computing Theory and Data Division Paul Scherrer Institute Switzerland Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics Faculty of Science University of Zagreb Zagreb10000 Croatia Institute of High Pressure Physics PAS WarsawPL-01-142 Poland CENTERA CEZAMAT Warsaw University of Technology WarsawPL-02-822 Poland

The ability to manipulate the surface states of topological insulators using electric or magnetic fields under ambient conditions is a key step toward their integration into future electronic and optoelectronic devices. Here, we demonstrate – using cyclotron resonance measurements on a tin-doped BiSbTe2S topological insulator – that moderate magnetic fields can quantize massless surface electrons into Landau levels even at room temperature. This finding suggests that surface-state electrons can behave as long-lived quasiparticles at unexpectedly high temperatures. Copyright © 2025, The Authors. All rights reserved.

关键词： Electron cyclotron resonance

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