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A fully mesh-independent non-linear topology optimization framework based on neural representations:Quasi-static problem

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science China(Physics,Mechanics & Astronomy) 2025年第04期Mechanics & Astronomy) .卷 123-147页

作者： Zeyu Zhang Yu Li Weien Zhou Wen Yao College of Aerospace science and Engineering National University of Defense Technology Defense Innovation institute Chinese Academy of Military Science Intelligent Game and decision Laboratory

In artificial intelligence(AI) for science, the AI-empowered topology optimization methods have garnered sustained attention from researchers and achieved significant development. In this paper, we introduce the implicit neural representation(INR) from AI and the material point method(MPM) from the field of computational mechanics into topology optimization, resulting in a novel differentiable and fully mesh-independent topology optimization framework named MI-TONR, and it is then applied to nonlinear topology optimization(NTO) design. Within MI-TONR, the INR is combined with the topology description function to construct the design model, while implicit MPM is employed for physical response analysis. A skillful integration is achieved between the design model based on the continuous implicit representation field and the analysis model based on the Lagrangian particles. Along with updating parameters of the neural network(i.e., design variables), the structural topologies iteratively evolve according to the responses analysis results and optimization functions. The computational differentiability is ensured at every step of MI-TONR, enabling sensitivity analysis using automatic differentiation. In addition, we introduce the augmented Lagrangian Method to handle multiple constraints in topology optimization and adopt a learning rate adaptive adjustment scheme to enhance the robustness of the optimization process. Numerical examples demonstrate that MI-TONR can effectively conduct NTO design under large loads without any numerical techniques to mitigate numerical instabilities. Meanwhile, its natural satisfaction with the no-penetration condition facilitates the NTO design of considering contact. The infinite spatial resolution characteristic facilitates the generation of structural topology at multiple resolutions with clear and continuous boundaries.

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Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions

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Journal of Environmental sciences 2025年第3期149卷 68-78页

作者： Qiang Zhang Xin Li Long Yu Lingxiao Wang Zhiqing Wen Pengchen Su Zhenli Sun Suhua Wang School of Environmental science and Engineering Guangdong University of Petrochemical TechnologyMaoming 525000China MOE Key Laboratory of Resources and Environmental System optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China

The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient *** this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous *** proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/***,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/*** fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data ***,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model ***,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride *** results demonstrate that the developed model excels in terms of accuracy and *** groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.

关键词： Machine learning Aluminum ion detection Fluorine ion detection Fluorescence probe K-means model

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Superradiant growth anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

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science China(Physics,Mechanics & Astronomy) 2025年第04期Mechanics & Astronomy) .卷 66-85页

作者： Nayun Jia Yin-Da Guo Gui-Rong Liang Zhan-Feng Mai Xin Zhang Department of Physics Southern University of Science and Technology Key Laboratory of Cosmology and Astrophysics (Liaoning) & College of sciences Northeastern University institute of Frontier and Interdisciplinary science Key Laboratory of Particle Physics and Particle Irradiation (Ministry of Education)Shandong University School of Materials science and Physics China University of Mining and Technology Kavli institute for Astronomy and Astrophysics Peking University Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi University Key Laboratory of data Analytics and optimization for Smart Industry (Ministry of Education) Northeastern University National Frontiers science Center for Industrial Intelligence and Systems optimization Northeastern University

Ultralight vector particles can form evolving condensates around a Kerr black hole （BH） due to superradiant *** study the effect of near-horizon reflection on the evolution of this system:by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode,we present explicit analytical expressions for the corrected spectrum and the superradiant instability ***,in high-spin BH cases,we identify an anomalous situation where the superradiance rate is temporarily increased by the reflection parameter R,which also occurs in the scalar scenario,but is largely magnified in vector condensates due to a faster growth rate in dominant *** point out that the conditio√n for the growth anomaly in the adiabatic case is that information carried per particle exceeds a certain value■.We further construct several featured quantities to illustrate it,and formalize the anomalyinduced gravitational wave strain deformation.

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A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal optimization Problems

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Chinese Journal of Electronics 2021年第1期30卷 145-152页

作者： ZHAO Shijie MA Shilin GAO Leifu YU Dongmei institute of optimization and decision College of Science Liaoning Technical University institute for optimization and decision Analytics Liaoning Technical University

To solve Multimodal optimization problems(MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework(NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics:quantum entanglement and quantum superposition. When given Proto-born particles(PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, *** if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.

关键词： optimisation quantum entanglement NMF-QE quantum superposition genetic algorithms search problems combination-born particles quantum physics evolutionary computation given Proto-born particles Multimodal optimization problems elite-born particles Novel Quantum entanglement-inspired meta-heuristic framework existing meta-heuristics

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A Novel Modified Tree-Seed Algorithm for High-Dimensional optimization Problems

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Chinese Journal of Electronics 2020年第2期29卷 337-343页

作者： ZHAO Shijie GAO Leifu TU Jun YU Dongmei institute of optimization and decision College of Science Liaoning Technical University institute for optimization and decision Analytics Liaoning Technical University

To efficiently handle high-dimensional continuous optimization problems, a Modified tree-seed algorithm(MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient(SDSC) and Local reinforcement strategy(LRS) based on gradient information of adjacentgeneration best trees. SDSC is dynamically decreased with iterations to adjust the produced domain of offspring seeds,for achieving the tradeoff between the global exploration and local exploitation. LRS strategy is to execute local exploitation process by employing gradient information of two best trees, for enhancing convergence efficiency and local optima avoidance with probabilities. The compared experimental results show the different effects of differenttype SDSC on MTSA, the faster convergence efficiency and the stronger robustness of the proposed MTSA.

关键词： Tree-seed algorithm Seed domain shrinkage Local reinforcement High-dimension optimization

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Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices

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Journal of Energy Chemistry 2025年第2期101卷 298-323页

作者： Bonghyun Jo Wenning Chen Hyun Suk Jung School of Advanced Materials science and Engineering Sungkyunkwan University SKKU institute of Energy science and Technology (SIEST) Sungkyunkwan University Department of Future Energy Engineering Sungkyunkwan University

Perovskite solar cells(PSCs) have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy ***,conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based *** increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machinelearning(ML) approaches to swiftly advance in the materials *** review succinctly outlines the fundamental ML procedures,techniques,and recent breakthroughs,particularly in predicting the physical characteristics of perovskite ***,it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of ***,this review highlights recent efforts in using characterization data for ML,exploring their correlations with material properties and device performance,which are actively being researched,but they have yet to receive significant ***,we provide future perspectives,such as leveraging Large Language Models(LLMs) and text-mining,to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.

关键词： Characterization data-driven machine learning Perovskite materials Perovskite solar cell

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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

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Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research institute of decision-Making science and Big data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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An Integrated External Archive Local Disturbance Mechanism for Multi-Objective Snake Optimizer

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Chinese Journal of Electronics 2024年第4期33卷 989-996页

作者： Leifu GAO Zheng LIU institute for optimization and decision Analytics Liaoning Technical University

It is an interesting research direction to develop new multi-objective optimization algorithms based on meta-heuristics. Both the convergence accuracy and population diversity of existing methods are not *** paper proposes an integrated external archive local disturbance mechanism for multi-objective snake optimizer(IMOSO) to overcome the above shortcomings. There are two improved strategies. The adaptive mating between subpopulations strategy introduces the special mating behavior of snakes with multiple husbands and wives into the original snake optimizer. Some positions are updated according to the dominated relationships between the newly created individuals and the original individuals. The external archive local disturbance mechanism is used to re-search partial non-inferior solutions with poor diversities. The perturbed solutions are non-dominated sorting with the generated solutions by the next iteration to update the next external archive. The main purpose of this mechanism is to make full use of the non-inferior solution information to better guide the population evolution. The comparison results of the IMOSO and 7 state-of-the-art algorithms on WFG benchmark functions show that IMOSO has better convergence and population diversity.

关键词： Accuracy Sociology Metaheuristics Diversity methods Benchmark testing Statistics Convergence

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5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

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Computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of data science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

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A Multi-Objective Particle Swarm optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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