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A DYNAMICAL VARIABLE-SEPARATION METHOD FOR PARAMETER-DEPENDENT DYNAMICAL SYSTEMS

SIAM Journal on Scientific computing 2025年第3期47卷 A1783-A1808页

作者： Chen, Liang Chen, Yaru Li, Qiuqi Zhou, Tao School of Mathematics Hunan University Changsha 410082 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

This paper proposes a dynamical variable-separation method for solving parameter-dependent dynamical systems. To achieve this, we establish a dynamical low-rank approximation for the solutions of these dynamical systems by successively enriching each term in the reduced basis functions via a greedy algorithm. This enables us to reformulate the problem to two decoupled evolution equations at each enrichment step, one of which is a parameter-independent PDE and the other a parameter-dependent ODE. These equations are directly derived from the original dynamical system and the previously separate representation terms. Moreover, the computational process of the proposed method can be split into an offline stage, in which the reduced basis functions are constructed, and an online stage, in which the efficient low-rank representation of the solution is employed. The proposed dynamical variable-separation method is capable of offering reduced computational complexity and enhanced efficiency compared to many existing low-rank separation techniques. Finally, we present various numerical results for linear/nonlinear parameter-dependent dynamical systems to demonstrate the effectiveness of the proposed method. © 2025 Society for Industrial and Applied mathematics.

关键词： dynamical variable-separation method low-rank approximation model order reduction parameter-dependent dynamical systems

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Effect of order–disorder transition on thermodynamic and electronic properties of σ and χ phases in W–Re alloy: the first-principles calculation

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Tungsten 2025年第1期7卷 195-213页

作者： Zhi-Peng Zhu Dian Jia William Yi Wang Jun-Lei Yin Xing-Yu Gao Shu-Feng Yang Hai-Feng Song Jin-Shan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical University Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics School of Metallurgical and Ecological Engineering University of Science and Technology Beijing

The Tungsten-Rhenium(W-Re) alloys,celebrated for their high melting point,strength at elevated temperatures,electrical resistivity,and radiation resistance,have been widely utilized in high-temperature components,aerospace,electronics,and nuclear *** constituents of the topologically close-packed(TCP) phases,the sigma phase(σ) and chi phase(χ) formed within W-Re alloys wield considerable influence on the mechanical properties and the stability of the *** calculations were utilized in the present work to explore the structural,thermodynamic,and electronic properties of both ordered and disordered configurations within the σ and χ phases,culminating in a systematic elucidation of the higher phase stability exhibited by the ordered *** is found that the bulk modulus of these two phases is directly proportional to the concentration of Re in the alloy,while the equilibrium volume is inversely *** thermodynamic parameters of the σ and χ phases are calculated via the mean-field potential *** similar trends observed in the isobaric heat capacity,enthalpy increment,and entropy change curves for these two phases suggest they possess comparable thermodynamic *** is noteworthy that the contribution of ionic vibrations predominantly affects the isobaric heat capacity,while the contribution of thermal electronic excitations increases linearly with *** the structure and thermodynamic properties of TCP phases in W-Re alloys at low temperatures has profound significance for optimizing material performance,microstructures features,establishing theoretical foundations,and predicting material behavior.

关键词： First-principles calculations Phase stability Thermodynamic properties W-Re alloy TCP phases

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Adaptive cyclic gradient methods with interpolation

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computational Optimization and Applications 2025年 1-25页

作者： Xie, Yixin Sun, Cong Yuan, Ya-Xiang Beijing100876 China Ministry of Education Beijing100876 China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

Gradient method is an important method for solving large scale problems. In this paper, a new gradient method framework for unconstrained optimization problem is proposed, where the stepsize is updated in a cyclic way. The Cauchy step is approximated by the quadratic interpolation. And the cycle for stepsize update is adjusted adaptively. Combining with the adaptive nonmonotone line search technique, we prove the global convergence of the proposed method. Furthermore, its sublinear convergence rate for convex problems and R-linear convergence rate for problems with quadratic functional growth property are analyzed. Numerical results show that our proposed algorithm enjoys good performances in terms of both computational cost and obtained function values. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Interpolation

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On greedy randomized coordinate updating iteration methods for solving symmetric eigenvalue problems

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Applied Numerical mathematics 2025年 216卷 76-97页

作者： Bai, Zhong-Zhi State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences P.O. Box 2719 Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In order to compute the smallest eigenvalue and its corresponding eigenvector of a large-scale, real, and symmetric matrix, we propose a class of greedy randomized coordinate updating iteration methods based on the principle that the indices of larger entries in absolute value of the current residual are selected with a higher probability and, with respect to this index set, the next iterate is updated from the current iterate along with the selected coordinate such that the corresponding entry of the residual is annihilated, resulting in fast convergence rates of the proposed iteration methods. Under appropriate conditions, we prove the convergence of both sequences of the Rayleigh-quotients and the acute angles between the iterates and the eigenvector in terms of the expectation. By numerical experiments, we show that this class of greedy randomized coordinate updating iteration methods are advantageous over the parameterized power method and the coordinate descent method in both iteration counts and computing times. Moreover, with theoretical analysis and computational performance, we confirm that the convergence property of this class of iteration methods can be improved significantly by suitably choosing the arbitrary nonnegative parameter involved in the greedy probability criterion. © 2025 IMACS

关键词： Eigenvalues and eigenfunctions

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Cervical Cancer Risk Prediction using SMOTE and Explainable AI

Cervical Cancer Risk Prediction using SMOTE and Explainable ...

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2025 International Conference on Pervasive computational Technologies, ICPCT 2025

作者： Agarwal, Priyansh Chaudhary, Annanya Sarvanan, T.R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence India Srm Institute of Science and Technology Faculty of Engineering & Tech. Department of Computational Intelligence India

ISBN: (纸本)9798331508685

Cervical cancer, accounting for 7.9% of cancers in women globally, is a critical health challenge, particularly due to its asymptomatic nature in early stages. This study proposes a machine learning-driven framework to predict cervical cancer risk using clinical data, addressing class imbalance through SMOTE and optimizing model performance via GridSearchCV. By evaluating five classifiers - K-Nearest Neighbors, Random Forest, Support Vector Classifier, Logistic Regression, and Decision Trees - this research identifies the Decision Tree model as the most robust, achieving superior precision, recall, and accuracy metrics. Additionally, Explainable AI techniques such as SHAP and LIME were employed to enhance interpretability, offering clinicians actionable insights into key risk factors driving predictions. The results demonstrate the effectiveness of machine learning in facilitating early detection and personalized treatment strategies for cervical cancer, thereby advancing its integration into clinical applications. © 2025 IEEE.

关键词： Lung cancer

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Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation

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Tsinghua science and Technology 2025年第2期30卷 585-599页

作者： Qiang Hua Jiachao Zhou Feng Zhang Chunru Dong Dachuan Xu College of Mathematics and Information Science Hebei UniversityBaoding 071002China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China

Integrating Knowledge Graphs(KGs)into recommendation systems as supplementary information has become a prevalent *** leveraging the semantic relationships between entities in KGs,recommendation systems can better comprehend user *** to the unique structure of KGs,methods based on Graph Neural Networks(GNNs)have emerged as the current technical ***,existing GNN-based methods struggle to(1)filter out noisy information in real-world KGs,and(2)differentiate the item representations obtained from the knowledge graph and bipartite *** this paper,we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation(namely AKDN)that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite *** particular,we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs,which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the ***,we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information,and leverage dual item representations to fuse them together for better capturing user behavior *** conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods,like Knowledge Graph Attention Network(KGAT)and Knowledge Graph-based Intent Network(KGIN).

关键词： Recommender System(RS) Knowledge Graph(KG) Graph Neural Networks(GNN) attention mechanism

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Hamiltonian boundary value methods applied to KdV-KdV systems

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Applied Numerical mathematics 2025年 214卷 1-27页

作者： Luo, Qian Xiao, Aiguo Luo, Xuqiong Yan, Xiaoqiang School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan 411105 China School of Mathematics and Statistics Changsha University of Science and Technology Changsha410114 China National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China

In this paper, we propose a highly accurate scheme for two KdV systems of the Boussinesq type under periodic boundary conditions. The proposed scheme combines the Fourier-Galerkin method for spatial discretization with Hamiltonian boundary value methods for time integration, ensuring the conservation of discrete mass and energy. By expanding the system in Fourier series, the equations are firstly transformed into Hamiltonian form, preserving the original Hamiltonian structure. Applying the Fourier-Galerkin method for semi-discretization in space, we obtain a large-scale system of Hamiltonian ordinary differential equations, which is then solved using a class of energy-conserving Runge-Kutta methods, known as Hamiltonian boundary value methods. The efficiency of this approach is assessed, and several numerical examples are provided to demonstrate the effectiveness of the method. © 2025 IMACS

关键词： Ordinary differential equations

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Chemical reaction mechanisms and models of energetic materials: A perspective

Energetic Materials Frontiers

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Energetic Materials Frontiers 2025年第1期6卷 129-144页

作者： Meng, Li Song, Qing-guan Yao, Chuang Zhang, Lei Pang, Si-ping School of Materials Science & Engineering Beijing Institute of Technology Beijing100081 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Institute of Chemical Materials China Academy of Engineering Physics Mianyang621999 China of Chongqing School of Materials Science and Engineering Yangtze Normal University Chongqing408100 China

Energetic materials (EMs) are a kind of metastable functional materials with certain potential barriers, overcoming which can quickly release the energy stored in EMs. A thorough understanding of reaction mechanisms and accurate quantification of reaction rates are fundamental issues for optimizing energy output, ensuring hazard mitigation, and assessing the safety levels of EMs. This perspective provides an overview of research progress in chemical reaction mechanisms and models, with a particular emphasis on organic EMs and reactive metals. Organic EMs are mainly composed of carbon, hydrogen, nitrogen, and oxygen elements, enabling supersonic and self-sustaining detonation reactions capable of significant energy output. The incorporation of reactive metals like aluminum, magnesium, and boron has been recently found to augment the combustion heat and explosion temperature of EM formulations, sparking heightened research interest. This perspective first presents both EMs’ reaction mechanisms revealed via multiscale simulations and experimental methods, including thermal decomposition, shock initiation, and post combustion. Then, quantitatively characterized expressions of the physical models derived from the revealed mechanisms, including mathematical expressions like elementary and phenomenological reaction kinetic models, and emerging data-driven machine learning models, are reviewed. Finally, the view of the application, existing problems, and further development directions are outlined. © 2024 The Authors

关键词： Reaction rates

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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2025 International Conference on Pervasive computational Technologies, ICPCT 2025

作者： Priya, M.Kani Aruru, Gunabhiram Vanam, Nikhil Suganiya, M. Salomi, M. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India

ISBN: (纸本)9798331508685

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection. © 2025 IEEE.

关键词： Lung cancer

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