IEEE Journal on Multiscale and Multiphysics Computational Techniques最新文献_第4页

MR2P: Diffusion Model-Driven Pixelated-Antenna Inverse Design Framework Generalizable Across Extensible Electromagnetic Figures of Merit MR2P：扩散模型驱动的像素天线逆设计框架，可推广到可扩展的电磁性能图

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-08-04 DOI: 10.1109/JMMCT.2026.3719661

Minje Kim;Euiho Shin;Hooyoung Kim;Taeyeong Yoon;Semin Lee;Jungsuek Oh

{"title":"MR2P: Diffusion Model-Driven Pixelated-Antenna Inverse Design Framework Generalizable Across Extensible Electromagnetic Figures of Merit","authors":"Minje Kim;Euiho Shin;Hooyoung Kim;Taeyeong Yoon;Semin Lee;Jungsuek Oh","doi":"10.1109/JMMCT.2026.3719661","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3719661","url":null,"abstract":"Electromagnetic (EM) inverse synthesis of pixelated antennas is a high-dimensional discrete problem that maps desired impedance and radiation responses to a fabrication-ready binary metallization layout. This paper introduces Multi-Response-to-Patch (MR2P), a conditional binary-diffusion framework that directly generates one-bit microstrip patch layouts from heterogeneous EM response specifications. Unlike full-wave or surrogate-assisted optimization pipelines that require a new search for each target, MR2P samples a binary layout through a single response-conditioned reverse process. The framework consists of a Multi-Response Encoder (MRE), pre-trained by a Multi-Response Masked AutoEncoder to embed broadband reflection coefficients <inline-formula><tex-math>$|S_{11}|$</tex-math></inline-formula> and frequency-wise <inline-formula><tex-math>$X$</tex-math></inline-formula>- and <inline-formula><tex-math>$Y$</tex-math></inline-formula>-polarized beam patterns into a unified latent sequence, and a Binary Diffusion Model (BDM), which denoises Bernoulli noise into a <inline-formula><tex-math>$32times 32$</tex-math></inline-formula> metal/void layout under cross-attention conditioning. Because the reverse process is defined directly on binary variables, no post-hoc thresholding is required. MR2P is trained on <inline-formula><tex-math>$24,mathrm{k}$</tex-math></inline-formula> HFSS-labeled one-bit layouts and evaluated by closed-loop full-wave re-simulation of generated layouts. Ablation studies verify the importance of the MRE pretraining, while artificial wideband and triple-band targets, nearest-neighbor comparisons, and a fabricated multi-response example demonstrate threshold-free, fabrication-ready inverse synthesis from quantitative EM specifications.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"515-526"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133460","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

Physics-Informed LSTM-KAN Hybrid Framework for Efficient Electromagnetic Forward Modeling 高效电磁正演建模的物理信息LSTM-KAN混合框架

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-03-14 DOI: 10.1109/JMMCT.2026.3693555

Jian Chen;Liangshuai Guo;Hongxia Ye

{"title":"Physics-Informed LSTM-KAN Hybrid Framework for Efficient Electromagnetic Forward Modeling","authors":"Jian Chen;Liangshuai Guo;Hongxia Ye","doi":"10.1109/JMMCT.2026.3693555","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3693555","url":null,"abstract":"Electromagnetic forward modeling often suffers from high computational cost and long simulation time. To address these challenges, this paper proposes a physics-informed spatiotemporal modeling framework that integrates the finite-difference time-domain (FDTD) scheme with deep learning. The proposed model synergistically combines a long short-term memory (LSTM) network for capturing temporal dependencies with a Kolmogorov–Arnold network (KAN) that employs trainable activation functions, thereby enhancing the nonlinear representation of electromagnetic field. A residual loss derived from Maxwell's equations is used to enforce physical consistency and improves the prediction reliability under complex scenarios. By learning electromagnetic field distributions in key regions, the framework replaces conventional explicit iterations with efficient spatiotemporal extrapolation. Numerical examples of multiple scenarios demonstrate the accuracy and stability of the method. In a ground-penetrating radar (GPR) scenario, this method reduces the computational cost by about 77.0% compared with gprMax, while achieving comparable accuracy, which validates its computational efficiency.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"348-359"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443283","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

Generative Antenna Design With Accuracy, Diversity, and Explainability via Dynamic Pseudo-Labeling 基于动态伪标记的精度、多样性和可解释性的生成天线设计

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/JMMCT.2026.3690046

Hoin Jung;Vinicius C. do Nascimento;Hongyang Liu;Xiaoqian Wang;Cheng-Kok Koh;Dan Jiao

引用次数: 0

Passenger-Aware Reduced-Order Digital Twin for Slice-Aware Fluidic Reconfigurable Automotive Antennas 基于乘客感知的片感知流控可重构汽车天线降阶数字孪生

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-08-21 DOI: 10.1109/JMMCT.2026.3726355

El Miloud Ar-Reyouchi;Saâdia Chabel;Omar Elmazria;Kamal Ghoumid

{"title":"Passenger-Aware Reduced-Order Digital Twin for Slice-Aware Fluidic Reconfigurable Automotive Antennas","authors":"El Miloud Ar-Reyouchi;Saâdia Chabel;Omar Elmazria;Kamal Ghoumid","doi":"10.1109/JMMCT.2026.3726355","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3726355","url":null,"abstract":"Future connected vehicles require antennas that remain reliable under service-dependent electromagnetic requirements and time-varying in-cabin loading. This paper presents a passenger-aware reduced-order digital twin for slice-aware fluidic reconfigurable automotive antennas. Its computational contribution is a confidence-aware prediction-and-state-selection framework that couples conductive-fluid antenna states, snapshot-based reduced prediction, passenger-induced dielectric loading, explicit acceptance/defer rules, and service-dependent state selection. The active slice is mapped to impedance, bandwidth, realized gain, angular coverage, pattern stability, and, where applicable, isolation and envelope correlation. Validation uses eight fluidic states, four passenger scenarios, and 41 frequency samples. Within the sampled interpolation domain, the method yields a reflection-coefficient RMSE of 0.19 dB, a radiation-pattern RMSE of 0.31 dB, and pattern correlation above 0.99. Extrapolative, experimental, real-time, and hardware-robustness claims are not made. Runtime acceleration is not quantified because reproducible hardware metadata and raw solver logs were unavailable.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"481-493"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985781","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

Investigation of Sidelobe Characteristics in 1-Bit Reconfigurable Intelligent Surfaces Considering Practical Unit Cell Non-Idealities 考虑实际单元非理想性的1位可重构智能曲面旁瓣特性研究

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-09-22 DOI: 10.1109/JMMCT.2026.3736468

Sudeb Bhattacharya;Aakash;Kumar Vaibhav Srivastava

引用次数: 0

Efficient Finite Element Modeling of Conformal Frequency-Selective Surface Radomes Using Rotational Symmetry 基于旋转对称的共形选频表面天线罩的高效有限元建模

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-03-07 DOI: 10.1109/JMMCT.2026.3691296

Jingwei Wang;Yanlin Zou;Yuntian Chen;Kun Liang

{"title":"Efficient Finite Element Modeling of Conformal Frequency-Selective Surface Radomes Using Rotational Symmetry","authors":"Jingwei Wang;Yanlin Zou;Yuntian Chen;Kun Liang","doi":"10.1109/JMMCT.2026.3691296","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3691296","url":null,"abstract":"Conformal Frequency Selective Surface (FSS) radomes have become increasingly crucial in modern radar systems due to their excellent band-pass characteristics and ability to reduce radar cross-section (RCS). The accurate simulation of the conformal radome requires large computational resources due to their electrically large size and complex geometric structure. In this article, an efficient finite element modeling method based on the first principles of symmetry is proposed, applying it to the electromagnetic simulation of conformal FSS radomes exemplified by <inline-formula><tex-math>$c_{N}$</tex-math></inline-formula> rotational symmetry. Utilizing projection operators from group theory, the method decomposes excitations into sub-excitations that adhere to the symmetry. This approach is versatile, extending beyond single rotational symmetry to accommodate any spatial symmetries and their combinations, thus surpassing the discrete Fourier expansion method’s capabilities. The technique significantly reduces the degrees of freedom (DOFs) to <inline-formula><tex-math>$1/N$</tex-math></inline-formula> of the original size, thus enhancing computational efficiency and simplifying geometric modeling and mesh complexity. With the proposed method, key metrics of conformal radomes, such as the RCS and transmittance/reflectance, can be effectively evaluated, thus supporting the design of conformal FSS radomes with arbitrary high rotational symmetry.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"226-236"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003411","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

Comparative Assessment of Graglia-Wilton-Peterson and Rao-Wilton-Glisson Basis Functions for Radiofrequency Coil Modeling in Magnetic Resonance Imaging 磁共振成像射频线圈建模中Graglia-Wilton-Peterson和Rao-Wilton-Glisson基函数的比较评估

IF 1.5

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-03-27 DOI: 10.1109/JMMCT.2026.3697593

Damiano Franzò;Ilias I Giannakopoulos;Adrien Merlini;Francesco Andriulli;Riccardo Lattanzi

引用次数: 0

Efficient Simulation of Doppler-Broadened Rydberg Quantum Receivers via Monte Carlo Wave Functions 利用蒙特卡罗波函数高效模拟多普勒加宽里德堡量子接收机

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-06-26 DOI: 10.1109/JMMCT.2026.3707907

Wenjie Ding;Xinyi Y. I. Xu;Guoda Xie;Xingang Ren;Yingsong Li;Wei E. I. Sha;Zhixiang Huang

{"title":"Efficient Simulation of Doppler-Broadened Rydberg Quantum Receivers via Monte Carlo Wave Functions","authors":"Wenjie Ding;Xinyi Y. I. Xu;Guoda Xie;Xingang Ren;Yingsong Li;Wei E. I. Sha;Zhixiang Huang","doi":"10.1109/JMMCT.2026.3707907","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3707907","url":null,"abstract":"Theoretical modeling of Rydberg atom-based microwave sensors is often limited by the high computational cost of the density matrix formalism (DMF), especially when resolving transient dynamics in Doppler-broadened vapor cells. We introduce an efficient direct time-domain numerical framework based on the Monte Carlo Wave Function (MCWF) method for Doppler-broadened Rydberg-atom simulations. By incorporating the Doppler effect as a stochastic initialization parameter within individual quantum trajectories, our approach naturally captures thermal broadening, thereby avoiding explicit velocity-grid integration in the MCWF calculation. This reformulates the propagation from a density-matrix representation whose state dimension scales quadratically with the number of levels to a trajectory-based wave-function representation with linear state dimension for each propagated trajectory. We demonstrate that this framework achieves nearly one order of magnitude reduction in runtime under comparable AT-splitting extraction accuracy, while maintaining agreement with the DMF results for the considered observables. The method is further applied to representative time-domain signal-recovery scenarios, including baseband symbol recovery for 8-ary amplitude-shift keying (8-ASK) signals. This work provides a scalable tool for designing and optimizing next-generation Rydberg quantum receivers.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"379-388"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486944","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

Rank-Limiting Strategies for Optimizing Tensor-Train Finite-Difference Time-Domain Simulations 张量-训练有限差分时域模拟优化的秩限制策略

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-06-24 DOI: 10.1109/JMMCT.2026.3707057

Jacob A. Scherzer;Fernando L. Teixeira

引用次数: 0

A Tandem Neural Network-Based Inverse Modeling Method for Fast and Accurate Estimation of Electromagnetic Model Parameters 一种基于串联神经网络的电磁模型参数快速准确反演方法

IF 2.1

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2026-01-01 Epub Date: 2026-06-25 DOI: 10.1109/JMMCT.2026.3707296

Kaan Kula;Tufan Coşkun Karalar

{"title":"A Tandem Neural Network-Based Inverse Modeling Method for Fast and Accurate Estimation of Electromagnetic Model Parameters","authors":"Kaan Kula;Tufan Coşkun Karalar","doi":"10.1109/JMMCT.2026.3707296","DOIUrl":"https://doi.org/10.1109/JMMCT.2026.3707296","url":null,"abstract":"In this paper, a neural network method is proposed for better estimation of simulation material parameters. Such improved estimations increase the accuracy of EM simulations. Estimating simulation material parameters from an electrical response is an ill-posed problem since multiple parameter combinations can exhibit almost the same electrical response. Such problems can be more effectively addressed by specific neural network topologies, such as tandem neural networks. Hence, a solution following this approach is proposed. As an example, the substrate model parameters of the cascaded T-resonators and the Beatty resonator are estimated using their scattering parameters. The proposed approach supports a wide range of microwave structures through a structure-independent training data generation process. As an alternative to the proposed tandem neural network that combines convolutional, transposed convolutional and fully connected neural networks, a second tandem neural network employing only fully connected neural networks was also realized as a reference model and the performance of the two is compared. The performance of the two is evaluated using synthetic test data obtained from EM simulations. The mean squared error of the proposed model is approximately <inline-formula><tex-math>$0.5 times 10^{-3}$</tex-math></inline-formula>, which is significantly lower than that of the reference model. The proposed method can be applied to measurement-based modeling, de-embedding fixture effects at high frequencies, production lot monitoring, among others.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"11 ","pages":"494-502"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033490","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