IEEE Journal on Multiscale and Multiphysics Computational Techniques最新文献

Editorial: Introducing Explaining the Unexplained 社论：介绍解释无法解释的

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-09-22 DOI: 10.1109/JMMCT.2025.3608780

Dan Jiao

引用次数: 0

Is DC Power Transmitted by Electromagnetic Waves? 直流电是由电磁波传输的吗？

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-09-09 DOI: 10.1109/JMMCT.2025.3608139

Mingyu Lu;Charan Litchfield

引用次数: 0

An Adaptive Time-Stepping Finite Element Method With Schur-Complement Preconditioning for Surge Simulation of Magnetic Components 磁元件喘振仿真的schur -补体预处理自适应时步有限元法

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-09-08 DOI: 10.1109/JMMCT.2025.3606993

Zhe Chen;Yanning Chen;Yi-Yao Wang;Hao-Xuan Zhang;Yin-Da Wang;Rongchuan Bai;Zhengwei Du;Yingzong Liang;Fang Liu;Hao Xie;Wen-Yan Yin

引用次数: 0

A Meshless Time-Domain Method for Geometric Uncertainty Quantification 几何不确定性量化的无网格时域方法

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-08-28 DOI: 10.1109/JMMCT.2025.3603902

Kiran Ravindran;Abhijith B. Narendranath;Kalarickaparambil Joseph Vinoy

{"title":"A Meshless Time-Domain Method for Geometric Uncertainty Quantification","authors":"Kiran Ravindran;Abhijith B. Narendranath;Kalarickaparambil Joseph Vinoy","doi":"10.1109/JMMCT.2025.3603902","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3603902","url":null,"abstract":"Numerical electromagnetic computations must often accommodate random geometric representations while handling biological tissues, and engineered components with manufacturing tolerances. Meshless time-domain radial point interpolation method (RPIM) offers advantages to quantitatively analyze such geometric uncertainties using polynomial chaos expansion (PCE). Formulations for geometric uncertainties may require variations in mesh or node distribution for each analyzed sample, leading to high computational requirement for re-meshing. The proposed geometric stochastic RPIM (G-SRPIM) overcomes this with a single domain model by expressing the shape function matrix of RPIM in a stochastic framework. The uncertainty is quantified in G-SRPIM through a novel way by which its random support domain moment matrices are organized in a block structure, and inverted using Schur's complement and Neumann approximation, exploiting the underlying symmetry. The proposed method is validated by analyzing a parallel plate waveguide with a slit exhibiting random variations, a realistic 3D bio-electromagnetic problem involving a section of human head, and an iris filter with random variations in its iris dimensions. Standard deviation upto <inline-formula><tex-math>$45 %$</tex-math></inline-formula> of the average inter-node distance is tested without jeopardizing the stability. The accuracy of our approach is compared with Monte-Carlo (MC) simulations on a deterministic RPIM using the Kolmogorov-Smirnov (KS) test. Additionally, results are compared with MC simulation on CST Studio Suite 2018 and stochastic collocation (SC). The proposed method exhibits superior execution time compared to SC and MC-based non-intrusive implementations, underscoring its efficiency and reliability in handling geometric uncertainties in microwave components.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"396-406"},"PeriodicalIF":1.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Method for Synthesizing Sparse Arrays With Well-Controlled Discrete Array Factors 具有良好控制的离散阵列因子的稀疏阵列的有效合成方法

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IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-08-26 DOI: 10.1109/JMMCT.2025.3602986

Ting Zang;Gaobiao Xiao

引用次数: 0

Knowledge-Based Bidirectional Recurrent Neural Network Approach for Efficient Prediction of Jitter in a Chain of CMOS Inverters 基于知识的双向递归神经网络方法有效预测CMOS逆变器链中的抖动

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-08-25 DOI: 10.1109/JMMCT.2025.3602632

Ahsan Javaid;Ramachandra Achar;Jai Narayan Tripathi

引用次数: 0

Performance Enhanced Copper-Graphene Hetero Interconnect Structures in Crossbar Arrays for Neuromorphic Computing 用于神经形态计算的交叉杆阵列中性能增强的铜-石墨烯异质互连结构

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-08-01 DOI: 10.1109/JMMCT.2025.3593872

Suyash Kushwaha;Chintu Bhaskara Rao;Shamini P R;Sourajeet Roy;Rohit Sharma

{"title":"Performance Enhanced Copper-Graphene Hetero Interconnect Structures in Crossbar Arrays for Neuromorphic Computing","authors":"Suyash Kushwaha;Chintu Bhaskara Rao;Shamini P R;Sourajeet Roy;Rohit Sharma","doi":"10.1109/JMMCT.2025.3593872","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3593872","url":null,"abstract":"In this paper, novel copper graphene heterogeneous interconnect structures are proposed which retain the ease of fabrication while having far better electrical performance when compared to the conventional copper interconnects. In the nanoscale regime, signal integrity (SI) of the copper interconnects degrades significantly. To address the signal integrity issues, these heterogeneous interconnects are developed at 7 nm technology nodes which are further used to make the crossbar arrays for neuromorphic computing. The proposed copper graphene heterogeneous interconnects were designed by stacking the layers of copper and multilayer graphene nanoribbons (MLGNRs) one over the other and a detailed signal integrity analysis is done based on the quantities like the per unit length Resistance, Insertion Loss (IL), Return Loss (RL), eye diagrams, surface charge density and volume current density. The results shows that the proposed interconnects outperformed the copper interconnects based on each and every SI quantity. Finally, in the application example, the best performing heterogeneous interconnects are used to create larger crossbar arrays with sizes 64 × 64, 128 × 128. Further, the key performance matrices such as the delay time, the rise time and the fall time are analyzed and compared with the conventional crossbars made from the copper interconnects. The results in application example proved that the heterogeneous interconnects performs better than the copper interconnects for neuromorphic computing.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"379-387"},"PeriodicalIF":1.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing DORT Method Performance in Time-Reversal Microwave Imaging Through Denoising Autoencoder 通过去噪自编码器增强时间反转微波成像中的DORT方法性能

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-07-15 DOI: 10.1109/JMMCT.2025.3589191

Hamed Rezaei;Amir Nader Askarpour;Abdolali Abdipour

引用次数: 0

Optimized Microwave Ablation With a Novel Applicator: Integration of Taguchi Neural Networks for Enhanced Predictive Accuracy of Ablation Zone 一种新型应用器优化微波消融：整合田口神经网络提高消融区预测精度

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-07-14 DOI: 10.1109/JMMCT.2025.3589163

Suyash Kumar Singh;Brij Kumar Bharti;Amar Nath Yadav;Ajay Kumar Dwivedi

{"title":"Optimized Microwave Ablation With a Novel Applicator: Integration of Taguchi Neural Networks for Enhanced Predictive Accuracy of Ablation Zone","authors":"Suyash Kumar Singh;Brij Kumar Bharti;Amar Nath Yadav;Ajay Kumar Dwivedi","doi":"10.1109/JMMCT.2025.3589163","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3589163","url":null,"abstract":"This study examines the computational challenges associated with modeling liver tumors using microwave ablation (MWA), while highlighting the limitations of conventional methods and advocating for the use of MWA in conjunction with artificial intelligence as a more promising approach. The proposed innovative antenna design, which comprises a coaxial line featuring a tapered outer conductor and a dipole antenna, aims to produce a nearly spherical ablation zone without the need for any additional matching network. Capable of operating at both 2.45 GHz and 5.8 GHz with minor structural modifications, it offers flexibility in tumor ablation systems. The research further incorporates and compares the sigmoidal model, a well-established computational method, and a recently developed parametric model for evaluating temperature-dependent properties in modeling the 3-D liver tissue, identifying differences in the ablation zone during MWA. Additionally, since both under and over ablation are major concerns during the MWA procedure, resulting in damage to healthy tissue and tumor recurrence, respectively, this study introduces a Taguchi Artificial Neural Networks (TNN) framework for the prediction of ablation zone in advance, thereby, significantly reducing the number of required training datasets without compromising performance metrics.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"348-359"},"PeriodicalIF":1.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Configuration and Performance Enhancement of Time-Modulated Circular Antenna Arrays 时间调制圆形天线阵列的优化配置与性能增强

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-07-10 DOI: 10.1109/JMMCT.2025.3587386

Satish Kumar;Gopi Ram;Durbadal Mandal;Rajib Kar

{"title":"Optimal Configuration and Performance Enhancement of Time-Modulated Circular Antenna Arrays","authors":"Satish Kumar;Gopi Ram;Durbadal Mandal;Rajib Kar","doi":"10.1109/JMMCT.2025.3587386","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3587386","url":null,"abstract":"In order to optimize the synthesis of Asymmetric Time-Modulated Circular Antenna Array (ATMCAA) and Symmetric Time-Modulated Circular Antenna Array (STMCAA), this work presents the Novel Particle Swarm Optimization Algorithm (NPSO). Inter-element spacing and uniform current excitation are maintained by regulating the switching time sequence and progressive phase delay of each element. A distinct cost function is developed for each of the two case studies. Using 20- and 36-element examples, several low side-lobe designs synthesized from ATMCAA and STMCAA are compared with traditional circular arrays. Through the manipulation of switching time sequence and progressive phase delay, the cost function is optimized to simultaneously reduce the side-lobe level (SLL) and directivity in ATMCAA and STMCAA. When it comes to antenna array synthesis, NPSO performs better than other algorithms, such as cat swarm optimization and invasive weed optimization. This study demonstrates how effective NPSO is at optimizing antenna arrays in order to improve higher communication reliability and signal quality.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"334-347"},"PeriodicalIF":1.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0