IEEE Journal on Multiscale and Multiphysics Computational Techniques最新文献_第2页

Variational Quantum Based Simulation of Cylindrical Waveguides

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-01-17 DOI: 10.1109/JMMCT.2025.3531134

Emanuel Colella;Benjamin A. Baldwin;Shaun F. Kelso;Luca Bastianelli;Valter Mariani Primiani;Franco Moglie;Gabriele Gradoni

{"title":"Variational Quantum Based Simulation of Cylindrical Waveguides","authors":"Emanuel Colella;Benjamin A. Baldwin;Shaun F. Kelso;Luca Bastianelli;Valter Mariani Primiani;Franco Moglie;Gabriele Gradoni","doi":"10.1109/JMMCT.2025.3531134","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3531134","url":null,"abstract":"The advent of noisy intermediate-scale quantum (NISQ) systems signifies an important stage in quantum computing development. Despite the constraints due to their limited qubit numbers and noise susceptibility, NISQ devices exhibit substantial potential to tackle complex computational challenges via hybrid classical-quantum algorithms. Among the various hybrid algorithms, variational quantum algorithms (VQAs) are gaining increasing attention due to their ability to solve highly complex, large-scale problems where classical algorithms fail. In particular, the variational quantum eigensolver (VQE) shows its potential in calculating the energies and ground states of large systems, where the complexity of solving such problems grows exponentially and becomes intractable for classical computers. At this regard, the aim of this paper is to extend the use of VQE for solving circular waveguide modes to verify their applicability to mathematically complex EM problems. In particular, we propose to calculate the fundamental and the some higher order modes for both transverse electric and transverse magnetic cases in circular waveguides. This is mathematically challenging due to the nature of geometry and the associated boundary conditions of circular structures. The results confirm the possibility of applying VQE for mathematically complex EM problems, announcing its potential to scale up and solve high-dimensional, large-scale EM problems where classical algorithms can fail.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"104-111"},"PeriodicalIF":1.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105959","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

Deep Learning-Based Partial Inductance Extraction of 3-D Interconnects

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-01-13 DOI: 10.1109/JMMCT.2025.3528484

Xiaofan Jia;Mingyu Wang;Qiqi Dai;Chao-Fu Wang;Abdulkadir C. Yucel

{"title":"Deep Learning-Based Partial Inductance Extraction of 3-D Interconnects","authors":"Xiaofan Jia;Mingyu Wang;Qiqi Dai;Chao-Fu Wang;Abdulkadir C. Yucel","doi":"10.1109/JMMCT.2025.3528484","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3528484","url":null,"abstract":"A physics-informed deep learning-based scheme is introduced for computing partial inductances of interconnects. This scheme takes a physics-based skin depth map and a geometry identifier of the interconnects as inputs and provides the current density distribution on the interconnects as the output. The predicted currents are then used to compute the partial self-resistances, self-inductances, and mutual-inductances of the interconnects. The proposed method leverages an Attention U-net, a U-shaped convolutional neural network with attention modules. During the training of Attention U-net, a specifically designed loss function is used to ensure the accurate modeling of the currents on the structure as well as ports. The accuracy, efficiency, and generalization ability of this physics-informed deep learning method are demonstrated via inductance extraction of the interconnects with and without a ground plane, including straight single interconnects, interconnects with sharp bends, parallel interconnects, and multiple conductor crossover buses. Numerical results show that the proposed scheme can predict the current density distribution of one interconnect scenario in 15.63 ms on GPU, 1157x faster than the physics-based solver, while providing self-inductances, mutual-inductances, and self-resistances of interconnects with around 1%, 3%, and 4% <inline-formula><tex-math>${{ell }_2}$</tex-math></inline-formula>-norm error, respectively.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"112-124"},"PeriodicalIF":1.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360947","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

EMI Shielding With Anisotropic Frequency Selective Surfaces: A Neural Network and Equivalent Circuit Approach

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2025-01-10 DOI: 10.1109/JMMCT.2025.3528076

Sairam SD;Sriram Kumar Dhamodharan

{"title":"EMI Shielding With Anisotropic Frequency Selective Surfaces: A Neural Network and Equivalent Circuit Approach","authors":"Sairam SD;Sriram Kumar Dhamodharan","doi":"10.1109/JMMCT.2025.3528076","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3528076","url":null,"abstract":"A multi-layer perceptron (MLP) model was applied to electromagnetic shielding to analyze a coupled ring anisotropic frequency selective surface (CRAFSS) using an equivalent circuit model. The shielding structure, based on a single-sided RT 5880 array, features unit elements with dimensions of <inline-formula><tex-math>$0.55lambda _{0} times 0.41lambda _{0}$</tex-math></inline-formula> at the resonant frequency. Various deep neural network (DNN) configurations with hidden layers were tested to achieve optimal results, reaching a minimal mean square error (MSE) of <inline-formula><tex-math>$1.012 times 10^{-4}$</tex-math></inline-formula>. The MLP was trained using input parameters such as S-parameters, resonant frequency, and shielding effectiveness, with the output being the dimensions of the proposed shielding structure. The dataset, built from capacitance and inductance values, was used for testing, training, and validation within the neural network, eventually employing inverse modeling for output prediction. The structure demonstrated stable bandwidth performance despite changes in the incidence angle of transverse magnetic (TM) and transverse electric (TE) polarizations, shifting from <inline-formula><tex-math>$theta$</tex-math></inline-formula> = <inline-formula><tex-math>$0^{0}$</tex-math></inline-formula> to <inline-formula><tex-math>$60^{0}$</tex-math></inline-formula>. The anisotropic FSS was developed and evaluated, with deep learning results and electromagnetic (EM) simulations playing a key role in the design process.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"94-103"},"PeriodicalIF":1.8,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105960","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

Scale-Compressed Technique in Finite-Difference Time-Domain Method for Multi-Layered Anisotropic Media 多层各向异性介质时域有限差分法中的比例压缩技术

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-12-31 DOI: 10.1109/JMMCT.2024.3524598

Yuxian Zhang;Yilin Kang;Naixing Feng;Xiaoli Feng;Zhixiang Huang;Atef Z. Elsherbeni

引用次数: 0

Experimental and Numerical Modeling of Magnetic Drug Targeting: Can We Trust Particle-Based Models? 磁性药物靶向的实验和数值模拟：我们可以信任基于粒子的模型吗？

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-12-20 DOI: 10.1109/JMMCT.2024.3520488

Angelika S. Thalmayer;Keyu Xiao;Paul Wolff;Georg Fischer

{"title":"Experimental and Numerical Modeling of Magnetic Drug Targeting: Can We Trust Particle-Based Models?","authors":"Angelika S. Thalmayer;Keyu Xiao;Paul Wolff;Georg Fischer","doi":"10.1109/JMMCT.2024.3520488","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3520488","url":null,"abstract":"The development of trustworthy simulation models is crucial for planning drug administration in magnetic drug targeting (MDT) interventions for future cancer treatment. In the MDT cancer therapy, the drug is bound to magnetic nanoparticles, which act as carriers and are guided through the cardiovascular system into the tumor region using an external magnetic field. Thus, the modeling represents a multiphysical problem and can be approached either by particle-based or concentration-based models. In this paper, both simulation approaches are implemented in COMSOL Multiphysics in a typical magnetic drug targeting scenario, verified by measurements, and compared among each other. Two different particle concentrations with and without an applied magnetic field of a Halbach array consisting of five permanent magnets in a tube flow system with a laminar velocity flow were investigated. Within this scope, an analytical model for calculating the system response for the detection of nanoparticles with a commercial susceptometer is derived, too. Considering the two implemented models and the investigated scenario, the concentration-based model shows a considerably better agreement with the experimental results for both with and without an applied magnetic field. The spatial resolution of the particle-based model is reduced due to the limited number of considered particles resulting in an inaccurate system response. Overall, the high number of new publications shows the need for research in this interdisciplinary research field to improve therapeutic success.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"69-84"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938138","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

Rigorous Indoor Wireless Communication System Simulations With Deep Learning-Based Radio Propagation Models 基于深度学习的无线电传播模型的严格室内无线通信系统仿真

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-12-09 DOI: 10.1109/JMMCT.2024.3506693

Stefanos Bakirtzis;Kehai Qiu;Jiming Chen;Hui Song;Jie Zhang;Ian Wassell

{"title":"Rigorous Indoor Wireless Communication System Simulations With Deep Learning-Based Radio Propagation Models","authors":"Stefanos Bakirtzis;Kehai Qiu;Jiming Chen;Hui Song;Jie Zhang;Ian Wassell","doi":"10.1109/JMMCT.2024.3506693","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3506693","url":null,"abstract":"Recently, there has been a surge in the development of data-driven propagation models. These models aspire to distill knowledge from propagation solvers or measured data and eventually become capable of predicting characteristics related to radiowave propagation. In this paper, we present the functionality of a generalizable and robust data-driven propagation model that enables efficient and reliable simulations of indoor wireless communication systems (IWCSs). In particular, we modify our previously introduced model, EM DeepRay, to consider the impact of antenna directivity, and we present a training and inference strategy that allows the simulation of large-scale and complicated IWCSs. Our data-driven model is trained over a rich data set comprising diverse building geometries, frequency bands, and antenna radiation patterns. Benchmarking its performance with that of a ray-tracer in complicated IWCSs with real-world measured data yields similar results that have a distinct advantage in terms of computational time. Ultimately, our work paves the way for replacing legacy IWCSs simulators, with high-fidelity artificial intelligence-based models.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"58-68"},"PeriodicalIF":1.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905782","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

Transfer Learning Based Rapid Design of Frequency and Dielectric Agile Antennas 基于迁移学习的频率和介电敏捷天线快速设计

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-11-29 DOI: 10.1109/JMMCT.2024.3509773

Aggraj Gupta;Uday K Khankhoje

{"title":"Transfer Learning Based Rapid Design of Frequency and Dielectric Agile Antennas","authors":"Aggraj Gupta;Uday K Khankhoje","doi":"10.1109/JMMCT.2024.3509773","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3509773","url":null,"abstract":"Deep learning frameworks are gaining prominence in the electromagnetics community for designing microwave and mm-wave devices. This paper presents a computationally efficient transfer learning technique for designing and scaling multi-band microstrip antennas to a desired dielectric and frequency of interest. The proposed methodology involves a two-step process. First, a pre-trained model trained extensively on air-filled microstrip antennas is used for knowledge transfer. This pre-trained model is fine-tuned with a limited set of dielectric simulations, reducing data acquisition costs. In the second step, the developed forward model serves as a surrogate to design dielectric-filled antennas using the Improved Binary Particle Swarm Optimization algorithm. In contrast to conventional methods, this approach enables the design of compact antennas across various dielectrics and frequency ranges, with a significantly reduced number of time-consuming dielectric simulations (88% fewer simulations) and a lower neural network training time (75% lesser time). We analyze the optimal ways of generating dielectric antenna datasets via scaling, and perform sensitivity analysis with respect to the antenna's physical parameters. We report simulation and experimental results for single and double band antennas fabricated using the proposed approach.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"47-57"},"PeriodicalIF":1.8,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810340","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

Critical-Point-Based Stability Analyses of Finite-Difference Time-Domain Methods for Schrödinger Equation Incorporating Vector and Scalar Potentials 含有矢量和标量势的Schrödinger方程时域有限差分法的临界点稳定性分析

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-11-20 DOI: 10.1109/JMMCT.2024.3502830

Eng Leong Tan;Ding Yu Heh

{"title":"Critical-Point-Based Stability Analyses of Finite-Difference Time-Domain Methods for Schrödinger Equation Incorporating Vector and Scalar Potentials","authors":"Eng Leong Tan;Ding Yu Heh","doi":"10.1109/JMMCT.2024.3502830","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3502830","url":null,"abstract":"This paper presents the critical-point-based stability analyses of finite-difference time-domain (FDTD) methods for Schrödinger equation incorporating vector and scalar potentials. Most previous FDTD formulations and stability analyses for the Schrödinger equation involve only the scalar potentials. On the other hand, the existing stability conditions that include both vector and scalar potentials were not thoroughly nor rigorously analyzed, hence they are inadequate for general cases. In this paper, rigorous stability analyses of the FDTD methods will be performed for Schrödinger equation in full 3D incorporating both vector and scalar potentials. New stability conditions are derived rigorously based on the critical points within the interior and boundary regions, while considering the local and global extrema across all variables. Two FDTD schemes are considered, of which one is updated entirely in complex form, and the other is decomposed into real and imaginary parts and updated in a leapfrog manner. Comparisons of the new stability conditions are made against those of prior works, highlighting the thoroughness, completeness and adequacy. Numerical experiments further validate the derived stability conditions and demonstrate their applicability in FDTD methods. Using these stability conditions, the FDTD methods are useful for simulations of quantum-electromagnetic interactions involving vector and scalar potentials.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"38-46"},"PeriodicalIF":1.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798015","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 Machine Learning for the Efficient Modeling of High-Frequency Devices 高频设备高效建模的物理信息机器学习

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-11-19 DOI: 10.1109/JMMCT.2024.3502062

Yanan Liu;Hongliang Li;Jian-Ming Jin

{"title":"Physics-Informed Machine Learning for the Efficient Modeling of High-Frequency Devices","authors":"Yanan Liu;Hongliang Li;Jian-Ming Jin","doi":"10.1109/JMMCT.2024.3502062","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3502062","url":null,"abstract":"In this paper, we present a machine learning technique based on analytic extension of eigenvalues and neural networks for the efficient modeling of high-frequency devices. In the proposed method, neural networks are used to learn the mapping between device's geometry and its modal equivalent circuit parameters. These circuit parameters are extracted from the eigen-decomposition of the deviceâs \u0000<inline-formula><tex-math>$Z$</tex-math></inline-formula>\u0000-parameters at a few sample frequencies. The eigenvalues and eigenvectors of the \u0000<inline-formula><tex-math>$Z$</tex-math></inline-formula>\u0000-matrix are analytically extended to other frequencies based on functional equations constructed from the lumped equivalent circuit model, from which the full electromagnetic response can be recovered. In addition to fully-connected neural network layers, our proposed model introduces an analytical projection branch based on AEE principles to maximize the information gain from samples in the training dataset. To improve the robustness and efficiency of the learning process, we introduce an adaptive gradient update algorithm. The overall model is end-to-end differentiable and can be integrated into gradient-based optimization methods. Numerical examples are provided to demonstrate the capability of the proposed method. Compared with traditional neural network-based models, the proposed approach achieves higher accuracy using fewer data samples and generalizes better to out-of-domain inputs.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"28-37"},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798028","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

Mechanical Antenna Simulations via FDTD to Characterize Mutual Depolarization 用时域有限差分法模拟机械天线互退极化

IF 1.8

IEEE Journal on Multiscale and Multiphysics Computational Techniques Pub Date : 2024-11-15 DOI: 10.1109/JMMCT.2024.3499369

Jesse Rivera;John Blaske;Zhi Yao;Ruoda Zheng;Gregory P. Carman;Yuanxun Ethan Wang

{"title":"Mechanical Antenna Simulations via FDTD to Characterize Mutual Depolarization","authors":"Jesse Rivera;John Blaske;Zhi Yao;Ruoda Zheng;Gregory P. Carman;Yuanxun Ethan Wang","doi":"10.1109/JMMCT.2024.3499369","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3499369","url":null,"abstract":"Antenna miniaturization is currently facing increased performance demands while simultaneously lacking a computational framework to drive robust designs. Future platforms must radiate farther, at lower frequency, and be increasingly compact. While mechanical resonance based piezoelectric antenna arrays are a viable candidate, detrimental mutual depolarization effects arise that must be characterized by multi-scale simulations, coupling the elastodynamic and EM wave physics. This work presents an algorithm capable of performing such full-wave simulations to provide design guidance to engineers wishing to mitigate mutual depolarization. The relevant dynamic systems of equations are discretized and put into a Finite Difference Time Domain (FDTD) scheme. This scheme exhibits electrodynamic unconditional stability and features heavily graded meshes to directly tackle the time and length scale disparity between the mechanical and EM waves. The algorithm was validated by comparison with experimental data and analytical solutions. Additionally, the algorithm compared well with predicted values for depolarization. Simulations demonstrated that spacing within piezoelectric antenna arrays should not be made too small, as to induce undue mutual depolarization, or too large, as to not allow sufficient elements to contribute to array dipole moment. Computational guidance is also provided based on the authors’ own experiences.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"8-27"},"PeriodicalIF":1.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777740","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