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Design and Validation of Zeroing Neural Network With Active Noise Rejection Capability for Time-Varying Problems Solving 认领 引用
1
作者 Yilin Shang Wenbo Zhang +1 位作者 Dongsheng Guo Shan Xue 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期256-266,共11页
Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode... Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems. 展开更多
关键词 active noise rejection matrix inversion robot application time-varying problems zeroing neural network
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Physics-informed neural networks with Poincaré-Lighthill-Kuo method for singular perturbation problems 认领 引用
2
作者 Qingyong Luo Lei Zhang Guowei He 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期211-222,共12页
Physics-informed neural networks(PINNs)have recently emerged as a powerful tool to solve differential equations for nonlinear mechanics.However,PINNs struggle with singular perturbation problems due to their locally a... Physics-informed neural networks(PINNs)have recently emerged as a powerful tool to solve differential equations for nonlinear mechanics.However,PINNs struggle with singular perturbation problems due to their locally abrupt behavior and singularities.The Poincaré-Lighthill-Kuo(PLK)method has been efficiently used to address these problems by applying perturbation expansions to both dependent and independent variables.This paper proposes a combination of the PLK method and PINNs,termed PLK-PINNs.The PLK-PINNs employ a parametric expression through two neural networks:one representing the mapping from parametric variables to independent variables,and the other approximating the solution of dependent variables with respect to parametric variables.Moreover,an auxiliary loss term is proposed to constrain the Jacobian determinant of the mapping within a constant sign interval to ensure the bijectivity of the mapping.The effectiveness of the proposed method is demonstrated through tests on typical singularity-shift and secular-term problems,with conventional PINNs in comparison. 展开更多
关键词 Physics-informed neural networks Singular perturbation problems Poincaré-Lighthill-Kuo method Parametric expressions
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Neural boundary shape functions in physics-informed neural networks for discontinuous and high-frequency problems 认领 引用
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作者 P.T.NGUYEN K.A.LUONG J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第2期423-442,共20页
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate... Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics. 展开更多
关键词 physics-informed neural network(PINN) boundary shape function(BSF) strong-form approach energy approach discontinuity high-frequency problem
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Investigation of forward and inverse problems in a nonlocal reverse-time nonlinear Schrödinger equation using physics-informed neural networks 认领 引用
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作者 Lianghui Hou Li Cheng +2 位作者 Yi Yang Junchao Chen Weiyi Shi 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第1期37-50,共14页
In this paper,we investigate data-driven bright soliton solutions of the nonlocal reverse-time nonlinear Schrödinger(NLS)equation and the parameter identification using the physically informed neural networks(PIN... In this paper,we investigate data-driven bright soliton solutions of the nonlocal reverse-time nonlinear Schrödinger(NLS)equation and the parameter identification using the physically informed neural networks(PINNs)algorithm.Accurate simulations and comparative analyses of relative and absolute errors are performed for two-soliton and four-soliton solutions including linear solitary waves and periodic waves.In the training process,the standard PINNs scheme is employed for linear solitary wave solutions,while the prior information is added at local sharp regions for periodic wave solutions due to the complicated collision behaviors.For the parameter identification,we accurately recognize the nonlinear coefficients of the nonlocal NLS equation from known solutions with different noises.These results reinforce the application of deep learning with the PINNs framework to successfully study nonlocal integrable systems. 展开更多
关键词 nonlocal reverse-time NLS equation physics-informed neural networks soliton solutions parameter identification
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Solving high-dimensional global optimization problems via solution space restructuring with neural network 认领 引用
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作者 N.VO T.LE-DUC +3 位作者 H.TANG H.NGUYEN-XUAN S.H.LEE J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1383-1400,共18页
It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-fre... It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-free techniques.However,the general framework for adaptively dealing with a particular optimization problem is commonly overlooked and thus still hidden in the literature.To overcome this limitation,a new approach assisted by the neural network(NN)is proposed for solving high-dimensional optimization issues.By restructuring the search space to optimize the objective function via a nonlinear mapping constructed by an autoencoder(AE),the surrogate solution space is constructed by a network training process and dynamically oriented to the optimal solution of the optimization issue.To enhance the optimization efficiency and address non-smooth problems,the classical metaheuristic grey wolf optimizer(GWO)and the adaptive moment estimation(Adam)are sequentially employed to complement the disadvantages of the constituted models.The effectiveness of the proposed approach is validated by solving a set of mathematical functions with 1000-dimensional and three large-scale truss design optimization problems.Several numerical experiments show that the solution space is reduced in terms of both size and complexity based on the restructuring procedure,in which the global optimal solution is still conserved,leading to better optimization efficiency when solving optimization problems with complex search domains with large dimensions.In addition,the hybrid optimizer has also been proven to be more effective when combined with the restructuring technique owing to the use of the Adam algorithm in the second phase. 展开更多
关键词 high-dimensional optimization solution space restructuring adaptive moment estimation(Adam) grey wolf optimizer(GWO) neural network(NN)
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Programming guide for solving constraint satisfaction problems with tensor networks 认领 引用
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作者 Xuanzhao Gao Xiaofeng Li Jinguo Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第5期71-90,共20页
Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either dire... Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either directly or by reducing it to other problems.This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices.We introduce some important CSPs and show how these problems are reduced to each other.We also show how to transform CSPs into tensor networks,how to optimize the tensor network contraction orders,and how to extract the solution space properties by contracting the tensor networks with generic element types.Examples are given,which include computing the entropy constant,analyzing the overlap gap property,and the reduction between CSPs. 展开更多
关键词 tensor networks constraint satisfaction problems problem reductions Julia
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A neural network based on novel equivalent model for linear complementarity problems 认领 引用
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作者 KE Yi-fen XIE Ya-jun +1 位作者 ZHANG Huai MA Chang-feng 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2025年第2期311-326,共16页
A family of neural networks is proposed to solve linear complementarity problems(LCP).The neural networks are constructed from the novel equivalent model of LCP,which is reformulated by utilizing the modulus and smoot... A family of neural networks is proposed to solve linear complementarity problems(LCP).The neural networks are constructed from the novel equivalent model of LCP,which is reformulated by utilizing the modulus and smoothing technologies.Some important properties of the proposed novel equivalent model are summarized.In addition,the stability properties of the proposed steepest descent-based neural networks for LCP are analyzed.In order to illustrate the theoretical results,we provide some numerical simulations and compare the proposed neural networks with existing neural networks based on the NCP-functions.Numerical results indicate that the performance of the proposed neural networks is effective and robust. 展开更多
关键词 linear complementarity problem neural network modulus stability
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Neural network solution based on the minimum potential energy principle for static problems of structural mechanics 认领 引用 被引量：1
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作者 Jiamin QIAN Lincong CHEN J.Q.SUN 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第6期1125-1142,共18页
This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissibl... This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissible function of the boundary-value problem(BVP),which satisfies all geometrical boundary conditions.We then prove that the admissible neural network solution also satisfies natural boundary conditions,and therefore all boundary conditions,when the stationarity condition of the variational principle is met.Numerical examples are presented to show the advantages and effectiveness of the VPINN in comparison with the physics-informed neural network(PINN).Another contribution of the work is the introduction of Gaussian approximation of the Dirac delta function,which significantly enhances the ability of neural networks to handle singularities,as demonstrated by the examples with concentrated support conditions and loadings.It is hoped that these structural examples are so convincing that engineers would adopt the VPINN method in their structural design practice. 展开更多
关键词 physics-informed neural network(PINN) variational physics-informed neural network(VPINN) structural statics admissible function Gaussian approximation
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Simultaneous imposition of initial and boundary conditions via decoupled physics-informed neural networks for solving initialboundary value problems 认领 引用
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作者 K.A.LUONG M.A.WAHAB J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第4期763-780,共18页
Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static... Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static problems;however,the simultaneous enforcement of I/BCs in dynamic problems remains challenging.To overcome this limitation,a novel approach called decoupled physics-informed neural network(d PINN)is proposed in this work.The d PINN operates based on the core idea of converting a partial differential equation(PDE)to a system of ordinary differential equations(ODEs)via the space-time decoupled formulation.To this end,the latent solution is expressed in the form of a linear combination of approximation functions and coefficients,where approximation functions are admissible and coefficients are unknowns of time that must be solved.Subsequently,the system of ODEs is obtained by implementing the weighted-residual form of the original PDE over the spatial domain.A multi-network structure is used to parameterize the set of coefficient functions,and the loss function of d PINN is established based on minimizing the residuals of the gained ODEs.In this scheme,the decoupled formulation leads to the independent handling of I/BCs.Accordingly,the BCs are automatically satisfied based on suitable selections of admissible functions.Meanwhile,the original ICs are replaced by the Galerkin form of the ICs concerning unknown coefficients,and the neural network(NN)outputs are modified to satisfy the gained ICs.Several benchmark problems involving different types of PDEs and I/BCs are used to demonstrate the superior performance of d PINN compared with regular PINN in terms of solution accuracy and computational cost. 展开更多
关键词 decoupled physics-informed neural network(dPINN) decoupled formulation Galerkin method initial-boundary value problem(IBVP) machine learning
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Tensor Network Algorithm to Solve Polaron Impurity Problems 认领 引用
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作者 Ruofan Chen Lei Gu Chu Guo 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第12期183-200,共18页
The polaron problem is a very old problem in condensed matter physics that dates back to the thirties,but still remains largely unsolved today,especially when electron–electron interaction is taken into consideration... The polaron problem is a very old problem in condensed matter physics that dates back to the thirties,but still remains largely unsolved today,especially when electron–electron interaction is taken into consideration.The presence of both electron–electron and electron–phonon interactions in the problem invalidates most existing numerical methods,which are either computationally too expensive or simply intractable.The continuous-time quantum Monte Carlo(CTQMC)methods could tackle this problem,but they are only effective on the imaginarytime axis.In this work,we present a method based on tensor networks and the path integral formalism to solve polaron impurity problems.As both the electron and phonon baths can be integrated out via the Feynman–Vernon influence functional in the path integral formalism,our method is free of bath discretization error.It can also flexibly work on imaginary time,Keldysh contour,and L-shaped Kadanoff contour.In addition,our method can naturally resolve several long-existing challenges:(i)non-diagonal hybridization function;(ii)measuring multi-time correlations beyond single-particle Green’s functions.We demonstrate the effectiveness and accuracy of our method with extensive numerical examples against analytic solutions,exact diagonalization,and CTQMC.We also perform full-fledged real-time calculations that have never been done before to our knowledge,which could serve as a benchmarking baseline for future method developments. 展开更多
关键词 tensor networks electron phonon interactions Feynman Vernon influence functional polaron problem condensed matter physics continuous time quantum Monte Carlo path integral formalism electron electron interaction
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Novel two⁃stage preflow algorithm for solving the maximum flow problem in a network with circles 认领 引用 被引量：1
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作者 DANG Yaoguo HUANG Jinxin +1 位作者 DING Xiaoyu WANG Junjie 《Journal of Southeast University(English Edition)》 EI CAS 2025年第1期91-100,共10页
The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that ... The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that at least one zero-flow arc must be present when the flow of the network reaches its maximum value.This result indicates that the maximum flow of the network will remain constant if a zero-flow arc within a circle is removed;therefore,the maximum flow of each network without circles can be calculated.The first stage involves identifying the zero-flow arc in the circle when the network flow reaches its maximum.The second stage aims to remove the zero-flow arc identified and modified in the first stage,thereby producing a new network without circles.The maximum flow of the original looped network can be obtained by solving the maximum flow of the newly generated acyclic network.Finally,an example is provided to demonstrate the validity and feasibility of this algorithm.This algorithm not only improves computational efficiency but also provides new perspectives and tools for solving similar network optimization problems. 展开更多
关键词 network with circles maximum flow zeroflow arc two-stage preflow algorithm
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Mitigating Hotspot Problem Using Northern Goshawk Optimization Based Energy Aware Multi-Hop Communication for Wireless Sensor Networks 认领 引用
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作者 S.Leones Sherwin Vimalraj J.Lydia 《China Communications》 SCIE EI CSCD 2025年第2期283-298,共16页
Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commo... Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures. 展开更多
关键词 clustering energy efficiency metaheuristics multihop communication network lifetime wireless sensor networks
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A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem 认领 引用 被引量：7
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作者 HUANG Tao LIU Jiang +1 位作者 CHEN Jianya LIU Yunjie 《China Communications》 SCIE CSCD 2014年第4期73-84,共12页
The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SD... The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem. 展开更多
关键词 network virtualization software- defined networking virtual network embedding topology-cognitive
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A solution framework for the experimental data shortage problem of lithium-ion batteries:Generative adversarial network-based data augmentation for battery state estimation 认领 引用 被引量：2
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作者 Jinghua Sun Ankun Gu Josef Kainz 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第4期476-497,共22页
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th... In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data. 展开更多
关键词 Lithium-ion battery Generative adversarial network Data augmentation State of health State of charge Data shortage
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Bayesian Network Reconstruction and Iterative Divergence Problem Solving Method Based on Norm Minimization 认领 引用
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作者 Kuo Li Aimin Wang +2 位作者 Limin Wang Yuetan Zhao Xinyu Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期617-637,共21页
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves... A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm2.The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation. 展开更多
关键词 Bayesian norm minimization network reconstruction iterative divergence sparsity
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Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems 认领 引用 被引量：139
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作者 Cosmin Anitescu Elena Atroshchenko +1 位作者 Naif Alajlan Timon Rabczuk 《Computers, Materials & Continua》 SCIE EI 2019年第4期345-359,共15页
We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy.In this procedure,a coarse grid of training points is used at the initial training s... We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy.In this procedure,a coarse grid of training points is used at the initial training stages,while more points are added at later stages based on the value of the residual at a larger set of evaluation points.This method increases the robustness of the neural network approximation and can result in significant computational savings,particularly when the solution is non-smooth.Numerical results are presented for benchmark problems for scalar-valued PDEs,namely Poisson and Helmholtz equations,as well as for an inverse acoustics problem. 展开更多
关键词 Deep learning adaptive collocation inverse problems artificial neural networks
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Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics 认领 引用 被引量：12
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作者 Qi-Hong Yang Yu Yang +3 位作者 Yang-Tao Deng Qiao-Lin He He-Lin Gong Shi-Quan Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第10期178-200,共23页
Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are ea... Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM. 展开更多
关键词 Neural network Reactor physics Neutron diffusion equation Eigenvalue problem Inverse power method
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Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Energy-efficient mechanism based on ACO for the coverage problem in sensor networks 认领 引用 被引量：5
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作者 黄如 朱杰 徐光辉 《Journal of Southeast University(English Edition)》 EI CAS 2007年第2期255-260,共6页
An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their level... An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained. 展开更多
关键词 sensor networks coverage problem ant colony optimization （ACO） energy-efficiency
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Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network 认领 引用 被引量：1
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作者 Zhen Xu Xin-Zhi Liu +1 位作者 Qing-Wei Chen Zi-Xing Wu 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第5期194-202,共9页
Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting proble... Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols. 展开更多
关键词 hunting problem multi-quadrotor system bearing constraint hierarchical network
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