Journal of Computational Science最新文献

ENOF:Outlier detection algorithm based on Elastic Neighborhood Outlier Factor ENOF：基于弹性邻域离群因子的离群点检测算法

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-12-13 DOI: 10.1016/j.jocs.2025.102766

Zhongping Zhang , Jinyu Dong , Kuo Wang , Zhongman Wang

Outlier detection is one of the core problems in the field of data mining. To address the limitations of existing outlier detection algorithms, which are often sensitive to the nearest neighbor parameter

k

, struggle with complex data distributions, and demonstrate low accuracy in detecting various types of outliers, we propose a novel outlier detection algorithm based on the Elastic Neighborhood Outlier Factor (ENOF). This method accounts for neighborhood density variations across different samples and introduces the concept of Mutual Nearest Neighbors to determine the optimal value of

k

when a sample reaches a steady state. By doing so, the algorithm more comprehensively captures the neighborhood information of each data object. A global radius is defined to characterize the elastic neighborhood of each sample. Based on this, the concept of elastic neighborhood density is introduced to identify global outliers. For the remaining samples, a thresholding strategy is employed, and an elastic neighborhood outlier factor is formulated by incorporating the number of mutual neighbors, which facilitates the further identification of local outliers. The proposed algorithm has been experimentally validated on both synthetic and real datasets, and its effectiveness is demonstrated through comparisons with several classical and novel algorithms.

异常点检测是数据挖掘领域的核心问题之一。针对现有离群值检测算法对最近邻参数k敏感、数据分布复杂、检测各类离群值准确率低的局限性，提出了一种基于弹性邻域离群值因子（ENOF）的离群值检测算法。该方法考虑了不同样本之间的邻域密度变化，并引入了相互近邻的概念，以确定样本达到稳态时k的最佳值。这样，算法可以更全面地捕获每个数据对象的邻域信息。定义了一个全局半径来表征每个样本的弹性邻域。在此基础上，引入弹性邻域密度的概念来识别全局异常值。对于剩余样本，采用阈值策略，结合相互邻居的数量，形成弹性邻域离群因子，便于进一步识别局部离群值。该算法在合成数据集和真实数据集上进行了实验验证，并与几种经典算法和新算法进行了比较，证明了其有效性。

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引用次数: 0

A local–global Graph Transformer model for fluid dynamics simulations 流体动力学仿真的局部-全局图形转换器模型

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-12-12 DOI: 10.1016/j.jocs.2025.102773

Jiamin Jiang , Jingrun Chen , Zhouwang Yang

Computational fluid dynamics is indispensable across numerous engineering disciplines, yet classical numerical methods often face efficiency bottlenecks that limit their suitability for real-time predictions and extensive parametric studies. Graph Neural Networks (GNNs) have shown promise as powerful deep learning alternatives that can naturally process unstructured data and capture nonlinear interactions within complex geometries. Nonetheless, GNNs struggle to effectively propagate global information due to their local aggregation mechanisms, significantly hindering their performance on PDE problems exhibiting long-range dependencies.

To overcome these challenges, we present a local–global Graph Transformer (LGGT) model that integrates message passing with global attention via a linear transformer. The LGGT architecture effectively preserves local details while capturing long-range dependencies without incurring quadratic computational complexity. We further propose a two-stage training strategy to mitigate temporal error accumulation during inference. Experiments on challenging fluid dynamics scenarios demonstrate that our LGGT neural solver delivers improved predictive accuracy, long-term stability, and generalization to unseen model configurations compared to baseline methods.

计算流体动力学在许多工程学科中都是不可或缺的，然而经典的数值方法往往面临效率瓶颈，限制了它们对实时预测和广泛参数研究的适用性。图神经网络（gnn）作为一种强大的深度学习替代方案，可以自然地处理非结构化数据，并捕获复杂几何中的非线性相互作用。然而，由于其局部聚合机制，gnn难以有效地传播全局信息，这极大地阻碍了它们在PDE问题上表现出长期依赖性的性能。为了克服这些挑战，我们提出了一个局部-全局图转换器（LGGT）模型，该模型通过线性转换器将消息传递与全局关注集成在一起。LGGT体系结构有效地保留了局部细节，同时捕获了远程依赖关系，而不会产生二次计算复杂性。我们进一步提出了一种两阶段训练策略来减轻推理过程中的时间误差积累。在具有挑战性的流体动力学场景中进行的实验表明，与基线方法相比，我们的LGGT神经求解器提供了更高的预测精度、长期稳定性和对未知模型配置的泛化。

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引用次数: 0

Algorithm for segmentation of multimodally distributed time series in accordance with their modes 多模态分布时间序列按模态分割算法

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-12-05 DOI: 10.1016/j.jocs.2025.102765

V.S. Petrakova, E.D. Karepova

The paper proposes an algorithm for dividing a time series with a multimodal distribution into long continuous segments corresponding to one of the modes of its distribution. We call such division of the series the Segmentation. The algorithm is a two-level classifier of time series elements: an element belongs to a segment, and a segment is assigned to a certain class. Each class is associated with a peak in the original histogram constructed for all elements of the series. We associate each histogram peak with a certain set of stable external conditions (operating modes) that affect the behavior of the observed variable. This refers us to the definition of non-stationarity of a series if this non-stationarity can be represented as a mixture of some distributions. The main idea for constructing the algorithm is to treat the elements of the series corresponding to one mode as a sample from a unimodal distribution. The two-level classifier takes into account the temporal nature of the series, i.e. the ordering of its elements in contrast to the sample. The algorithm operates under the assumption that the distribution of data in each resulting class is close to normal. The article proposes testing the algorithm on synthetic and real data.

本文提出了一种将具有多模态分布的时间序列划分为与其分布的一种模态相对应的长连续段的算法。我们把这种系列的划分称为分割。该算法是时间序列元素的两级分类器：一个元素属于一个片段，一个片段被分配到某个类。每个类都与为该系列的所有元素构建的原始直方图中的一个峰值相关联。我们将每个直方图峰值与影响观察变量行为的一组稳定的外部条件（操作模式）联系起来。这指的是一个序列的非平稳性的定义，如果这种非平稳性可以表示为一些分布的混合。构造该算法的主要思想是将序列中对应于一个模态的元素视为来自单峰分布的样本。两级分类器考虑了序列的时间性质，即与样本相比，其元素的顺序。该算法在假设每个结果类中的数据分布接近正态分布的情况下运行。本文提出在综合数据和实际数据上对该算法进行测试。

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引用次数: 0

A liquid neural network with physical evolution for variable continuous time series prediction 一种具有物理演化的液体神经网络用于变量连续时间序列预测

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-27 DOI: 10.1016/j.jocs.2025.102757

Kui Qian, Yue Deng, Zhengyan Li, Xiulan Wen

With the advantages of real-time computation and transparent decision-making process, Liquid time-constant neural networks (LTCs) perform well in modeling time-varying systems, but they also face problems such as the limitations of dependent learning capabilities and the adaptability to different data distributions. In order to improve the model learning performance, a liquid neural network with physical evolution for variable continuous time series prediction is proposed. Firstly, a simulation synaptic neural transmission model is combined with Hamilton evolution to establish a dynamic evolution model with physical states for neural information transfer. Then an explicit time-dependent Hamiltonian closed-form continuous-depth network (HCFC) is constructed to handle the transmission processing. The implicit Hamilton canonical equations are utilized to model the sophisticated nonlinear transformations experienced by the information as it propagates to the current neuron. The experimental results show that the HCFC model can improve the neural network dynamic property, enhance the learning performance, and provide superior performance in the prediction of complex continuous time series compared with the state-of-the-art (SOTA) methods.

液体时间常数神经网络（Liquid time-constant neural networks, LTCs）具有计算实时性和决策过程透明的优点，在时变系统建模方面表现良好，但也面临依赖学习能力的限制和对不同数据分布的适应性等问题。为了提高模型的学习性能，提出了一种具有物理演化的液体神经网络用于变量连续时间序列预测。首先，将模拟突触神经传递模型与Hamilton进化相结合，建立具有物理状态的神经信息传递动态进化模型；然后构造了一个显式时变哈密顿闭型连续深度网络（HCFC）来处理传输过程。隐式汉密尔顿正则方程被用来模拟复杂的非线性转换经历的信息，因为它传播到当前神经元。实验结果表明，HCFC模型可以改善神经网络的动态特性，提高学习性能，在复杂连续时间序列的预测方面比SOTA方法具有更好的性能。

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引用次数: 0

Enhancing urban solar photovoltaic system performance evaluation through a disc spherical fuzzy aggregation framework 通过圆盘球形模糊聚合框架增强城市太阳能光伏系统性能评价

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-26 DOI: 10.1016/j.jocs.2025.102758

Shahzaib Ashraf , Muhammad Shakir Chohan , Wania Iqbal , Vladimir Simic , Dragan Pamucar , Nebojsa Bacanin

The integration of solar photovoltaic (PV) systems in urban environments promises great potential for sustainable energy applications. However, the unique characteristics of cities, the varieties of weather that occur at the place, and technology inefficiency make performance evaluation difficult. This paper sought to address the pressing need for a robust performance evaluation framework for urban solar PV systems by developing a disc spherical fuzzy aggregation framework. It develops basic algebraic aggregation operations in the framework of the disc spherical fuzzy set (D-SFSs), proving their completeness and describing their essential characteristics. These new operators conceived to operate on D-SFSs furnish theoretical robustness and provide the foundation for decisions made. A shining novel disc spherical fuzzy method is developed namely combinative distance-based assessment (CODAS) in D-SFS. A case study regarding the application of this model in the assessment of performance by urban solar PV systems is being conducted, thus proving the application aspect. Results come out positive in interpreting the decision-making dilemma and differences among several experts. This would, therefore, encourage various sectors to expand the use of D-SFSs in decision support systems and similar areas by showing how useful they can be in actual situations.

太阳能光伏系统在城市环境中的整合为可持续能源应用提供了巨大的潜力。然而，城市的独特特征、当地天气的变化以及技术的低效使得绩效评估变得困难。本文试图通过开发一个圆盘球形模糊聚合框架来解决对城市太阳能光伏系统性能评估框架的迫切需求。在圆盘球形模糊集（D-SFSs）的框架中发展了基本的代数聚集运算，证明了它们的完备性并描述了它们的本质特征。这些设想在d - sss上操作的新算子提供了理论上的鲁棒性，并为决策提供了基础。在D-SFS中，提出了一种新颖的圆盘球面模糊评价方法——基于距离的组合评价方法（CODAS）。目前正在对该模型在城市太阳能光伏系统性能评价中的应用进行案例研究，验证了该模型的应用前景。结果在解释决策困境和几位专家之间的差异方面是积极的。因此，这将鼓励各部门在决策支持系统和类似领域扩大使用D-SFSs，显示它们在实际情况中如何有用。

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引用次数: 0

Exploring network-level indicators for project analysis — A comparison of real and synthetic projects 探索项目分析的网络级指标——真实项目和综合项目的比较

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-26 DOI: 10.1016/j.jocs.2025.102745

Zsolt T. Kosztyán

This study introduces a novel perspective on project network analysis by incorporating network theory and graph analysis to identify network-level indicators for activity-on-node project networks. A key contribution lies in the comparison between real and synthetic projects on the basis of project and network-level indicators, revealing distinct variations. The findings underscore that real projects demonstrate lower flexibility and efficiency than synthetic counterparts, impacting project scheduling and resource allocation. Moreover, the study suggests employing supervised and unsupervised learning classification methods to categorize projects on the basis of indicator values, enhancing project selection and prioritization processes. By bridging the gap between network-level indicators and project aspects, this research enriches the literature by offering fresh insights and resources for project managers to optimize project network structure and performance.

本研究将网络理论与图分析相结合，引入项目网络分析的新视角，以确定节点上活动项目网络的网络级指标。一个关键的贡献是在项目和网络一级指标的基础上比较了实际项目和综合项目，揭示了明显的差异。研究结果强调，真实项目的灵活性和效率低于合成项目，影响了项目的进度和资源分配。此外，研究建议采用监督学习和无监督学习分类方法，根据指标值对项目进行分类，加强项目选择和优先排序过程。通过弥合网络级指标与项目方面之间的差距，本研究丰富了文献，为项目经理优化项目网络结构和绩效提供了新的见解和资源。

引用次数: 0

Two improved physics-informed Neural Networks for solving Burgers equation 两个改进的物理信息神经网络求解汉堡方程

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-24 DOI: 10.1016/j.jocs.2025.102756

Zeyue Zhang , Chunlei Ruan , Zhijun Liu

Burgers equation, a simplified mathematical model for the Navier–Stokes equation, is often discussed in Computational Fluid Dynamics (CFD). The Burgers equation is a nonlinear convection–diffusion equation in mathematics, its hyperbolic characteristic challenges the numerical methods. Previous studies with the Physics-Informed Neural Networks (PINNs) on Burgers equation have focused on the relatively high viscosity. In that case, the hyperbolicity of Burgers equation is weak and convergent solutions can be obtained. In this study, we attempt to solve Burgers equation with vanishingly small viscosity and with no viscosity. To obtain the convergent and accurate solutions, we present two improved PINNs, one is the PINNs with time segmentation and the other is PINNs with the Weighted Essential Non-oscillatory (WENO) indicator. The former one is to divide the entire time domain into multiple continuous time periods and to iterate training in each time period. The latter is proposed to deal with the discontinuity. The WENO indicator is used to find the non-smooth region in which more training points should be added. Two numerical examples, including one-dimensional Burgers equation with vanishingly small viscosity with Dirichlet boundary conditions and one-dimensional non-viscous Burgers equation with Dirichlet boundary conditions, are carried out. By comparing with the results obtained by the 5th order WENO-Z method and the other three PINNs methods, the validity and high accuracy of the improved PINNs are proved.

Burgers方程是Navier-Stokes方程的简化数学模型，是计算流体动力学（CFD）中经常讨论的问题。Burgers方程是数学中的非线性对流扩散方程，其双曲性对数值方法提出了挑战。先前使用物理信息神经网络（pinn）对Burgers方程的研究主要集中在相对较高的粘度上。在这种情况下，Burgers方程的双曲性是弱的，可以得到收敛解。在本研究中，我们尝试求解具有极小黏度和无黏度的Burgers方程。为了得到收敛且精确的解，我们提出了两种改进的pin - ns，一种是带时间分割的pin - ns，另一种是带加权基本非振荡（WENO）指标的pin - ns。前一种方法是将整个时域划分为多个连续时间段，在每个时间段内迭代训练。后者是为了处理不连续性而提出的。WENO指标用于寻找需要增加更多训练点的非光滑区域。给出了具有Dirichlet边界条件的一维粘性消失的Burgers方程和具有Dirichlet边界条件的一维非粘性Burgers方程两个数值算例。通过与5阶WENO-Z方法和其他3种pinn方法的结果比较，证明了改进的pinn方法的有效性和较高的精度。

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引用次数: 0

Computational modeling of incompressible two-phase fluid dynamics via the conservative Allen–Cahn framework on a virtual cube 虚拟立方体上不可压缩两相流体动力学的保守Allen-Cahn框架计算模型

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-24 DOI: 10.1016/j.jocs.2025.102755

Ahmed Zeeshan , Juho Ma , Zhengang Li , Xinpei Wu , Junseok Kim

We present a robust computational algorithm for the simulation of incompressible two-phase fluid flows on a virtual cubic surface, i.e., the models of incorporating the conservative Allen–Cahn (CAC) equation into the Navier–Stokes (NS) equation. By using a phase-field approach, the proposed method effectively captures the evolution of complex interfaces among distinct fluid phases instead of explicit interface tracking. The projection method is combined with the finite difference method (FDM) to solve the governing equations in an efficient manner. In addition, a multigrid solver is adopted to handle the pressure Poisson equation, which improves computational accuracy and reduces computational cost. The virtual cubic surface is modeled as a two-dimensional unfolded domain to facilitate straightforward discretization while preserving geometric fidelity. Numerical experiments, including benchmark shear flow and vortex dynamics on the cubic surface, back up the efficacy of the method in handling two-phase flows. The computational results validate that the proposed scheme has significant potential to advance the simulation of multiphase incompressible flows on curved or complex surfaces. This approach provides an effective numerical method applicable to various scientific and engineering problems.

本文提出了一种鲁棒的模拟虚拟立方表面上不可压缩两相流体流动的计算算法，即将保守的Allen-Cahn （CAC）方程与Navier-Stokes （NS）方程相结合的模型。该方法采用相场方法，可以有效地捕捉不同流体相之间复杂界面的演化，而不是显式的界面跟踪。将投影法与有限差分法相结合，有效地求解了控制方程。此外，采用多网格求解器处理压力泊松方程，提高了计算精度，降低了计算成本。虚拟立方体表面建模为二维展开域，以方便直接离散，同时保持几何保真度。数值实验，包括基准剪切流和立方表面上的涡动力学，验证了该方法处理两相流的有效性。计算结果表明，该方法对复杂曲面上多相不可压缩流动的模拟具有重要的推动作用。该方法提供了一种适用于各种科学和工程问题的有效数值方法。

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引用次数: 0

Safe merging aircraft flows in multi-route schemes 多航路方案下的安全合流

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-13 DOI: 10.1016/j.jocs.2025.102741

Arseniy A. Spiridonov

The paper considers the problem of creating a conflict-free schedule of aircraft arrivals at checkpoints of an air route scheme for several incoming aircraft flows in the case of multi-stage multi-route merging. Here, “multi-stage” means that an aircraft may sequentially pass several points of merging with other aircraft flows. “Multi-route” means that an aircraft may have several routes leading it from the entry point of its flow to the final point of the scheme. The assumptions adopted in the problem allow a consideration of realistic air route schemes. The main result of the paper is a methodology for constructing a description of the problem in the framework of mixed integer linear programming. The case of several runways is not included, but the model can be extended to cover this case. Models obtained by the suggested approach are computed numerically by means of the optimization library Gurobi. The simulation results and performance of the computational procedure for Koltsovo airport are presented.

本文研究了在多阶段多航路合并的情况下，针对多个进港航路的航路方案，建立无冲突的飞机到达检查点时间表的问题。在这里，“多级”是指一架飞机可能依次通过与其他飞机流合并的几个点。“多航线”是指一架飞机可能有多条航线将其从流的入口点引导到方案的最终点。问题中采用的假设允许考虑实际的航路方案。本文的主要成果是一种在混合整数线性规划框架下构造问题描述的方法。几个跑道的情况不包括在内，但该模型可以扩展到涵盖这种情况。利用优化库Gurobi对该方法得到的模型进行了数值计算。给出了Koltsovo机场的仿真结果和计算过程的性能。

引用次数: 0

ELM-UNet: An efficient and lightweight Vision Mamba UNet for skin lesion segmentation ELM-UNet：用于皮肤病变分割的高效轻量级视觉曼巴UNet

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2025-11-11 DOI: 10.1016/j.jocs.2025.102743

Songling Xia , Hongwei Deng , Feng Chen

Skin lesion segmentation plays a critical role in the early detection and treatment of skin cancer. Although models based on CNNs and Transformers have achieved notable progress in image segmentation, they still exhibit some limitations. Specifically, CNNs struggle with capturing long-range dependencies effectively, while Transformers suffer from high computational costs due to the self-attention mechanism. Recently, Mamba as a representative State Space Models (SSMs), has drawn attention for its ability to efficiently model long-range dependencies with lower computational overhead. To address these challenges, we propose ELM-UNet, an efficient and lightweight skin lesion segmentation method based on the Mamba architecture. We introduce the LKA-MLP Synergy Module (LMSM), which significantly enhances the model’s ability to capture fine details and handle complex regions in skin lesion images, thus improving segmentation precision. Additionally, we present the SSM-Conv Fusion Module (SSM-CFM), which effectively combines the strengths of SSM in modeling long-range dependencies and convolution operation in local feature extraction, further boosting lesion feature representation. The experimental results show that compared to UltraLight VM-UNet, on the ISIC2017 dataset, ELM-UNet achieves improvements of 2.04%, 1.21%, and 1.61% in mIoU, DSC, and Sen, respectively. On the ISIC2018 dataset, it achieves improvements of 1.81%, 1.10%, and 2.36% in mIoU, DSC, and Sen, respectively.

皮肤病灶分割在皮肤癌的早期发现和治疗中起着至关重要的作用。尽管基于cnn和Transformers的模型在图像分割方面取得了显著的进展，但它们仍然存在一定的局限性。具体来说，cnn很难有效地捕获远程依赖关系，而变形金刚由于自关注机制而遭受高计算成本的困扰。最近，Mamba作为一种代表性的状态空间模型（ssm），因其能够以较低的计算开销高效地建模远程依赖关系而引起了人们的关注。为了解决这些问题，我们提出了一种基于Mamba结构的高效、轻量级的皮肤损伤分割方法ELM-UNet。我们引入了LKA-MLP协同模块（LMSM），显著增强了模型对皮肤病变图像中精细细节的捕捉能力和对复杂区域的处理能力，从而提高了分割精度。此外，我们提出了SSM- conv融合模块（SSM- cfm），该模块有效地结合了SSM在远程依赖关系建模方面的优势和卷积运算在局部特征提取方面的优势，进一步增强了病灶特征的表征。实验结果表明，与UltraLight VM-UNet相比，在ISIC2017数据集上，ELM-UNet在mIoU、DSC和Sen上分别提高了2.04%、1.21%和1.61%。在ISIC2018数据集上，mIoU、DSC和Sen分别提高了1.81%、1.10%和2.36%。

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引用次数: 0