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.
{"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}
Pub Date : 2024-11-29DOI: 10.1109/JMMCT.2024.3509773
Aggraj Gupta;Uday K Khankhoje
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.
{"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}
Pub Date : 2024-11-20DOI: 10.1109/JMMCT.2024.3502830
Eng Leong Tan;Ding Yu Heh
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.
{"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}
Pub Date : 2024-11-19DOI: 10.1109/JMMCT.2024.3502062
Yanan Liu;Hongliang Li;Jian-Ming Jin
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 $Z$ -parameters at a few sample frequencies. The eigenvalues and eigenvectors of the $Z$ -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.
{"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}
Pub Date : 2024-11-15DOI: 10.1109/JMMCT.2024.3499369
Jesse Rivera;John Blaske;Zhi Yao;Ruoda Zheng;Gregory P. Carman;Yuanxun Ethan Wang
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.
{"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}
Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.
{"title":"Quantum Optimization of Reconfigurable Intelligent Surfaces for Mitigating Multipath Fading in Wireless Networks","authors":"Emanuel Colella;Luca Bastianelli;Valter Mariani Primiani;Zhen Peng;Franco Moglie;Gabriele Gradoni","doi":"10.1109/JMMCT.2024.3494037","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3494037","url":null,"abstract":"Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"403-414"},"PeriodicalIF":1.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In addressing the challenges posed by low-frequency airborne transient electromagnetics (ATEM), it is necessary to take into account the considerations of accuracy, computational efficiency, and the scale and intricacy of the physical domain. This becomes particularly crucial when dealing with large-scale, complex issues, with the aim of mitigating the computational resource burden associated with managing such complexities. In order to further meet the aforementioned criteria, a Perfectly Matched Monolayer (PMM) model has been introduced into the Random Forest Regression (RFR) framework. The RFR-based PMM model has demonstrated exceptional accuracy through the utilization of Bagging's integrated learning methodology, while also reducing the computational resource requirements for processing time. In comparison to traditional machine learning models, our model has exhibited significant advantages in terms of training stability, model efficiency, and parallelization capabilities. To verify and establish the reliability of this approach, three-dimensional numerical simulations of the ATEM problem were conducted. The proposed model in this study has exhibited superior accuracy, efficiency, and versatility in addressing the low-frequency ATEM problem, integrating with the FDTD method.
{"title":"Efficient Physical Truncation of Low-Frequency ATEM Problems in Specific Geometries by Using Random Forest Regression Based PMM Model","authors":"Naixing Feng;Shuiqing Zeng;Huan Wang;Yuxian Zhang;Zhixiang Huang","doi":"10.1109/JMMCT.2024.3491835","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3491835","url":null,"abstract":"In addressing the challenges posed by low-frequency airborne transient electromagnetics (ATEM), it is necessary to take into account the considerations of accuracy, computational efficiency, and the scale and intricacy of the physical domain. This becomes particularly crucial when dealing with large-scale, complex issues, with the aim of mitigating the computational resource burden associated with managing such complexities. In order to further meet the aforementioned criteria, a Perfectly Matched Monolayer (PMM) model has been introduced into the Random Forest Regression (RFR) framework. The RFR-based PMM model has demonstrated exceptional accuracy through the utilization of Bagging's integrated learning methodology, while also reducing the computational resource requirements for processing time. In comparison to traditional machine learning models, our model has exhibited significant advantages in terms of training stability, model efficiency, and parallelization capabilities. To verify and establish the reliability of this approach, three-dimensional numerical simulations of the ATEM problem were conducted. The proposed model in this study has exhibited superior accuracy, efficiency, and versatility in addressing the low-frequency ATEM problem, integrating with the FDTD method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"1-7"},"PeriodicalIF":1.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753858","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}
Pub Date : 2024-10-29DOI: 10.1109/JMMCT.2024.3487779
Chang Yang;Dan Jiao
In this paper, we develop a kernel-independent and purely algebraic method, Nested Pseudo-Skeleton Approximation (NPSA) algorithm, to generate a low-rank ${mathcal H}^{2}$ -matrix representation of electrically large surface integral equations (SIEs). The algorithm only uses $O(NlogN)$ entries of the original dense SIE matrix of size $N$ to generate the ${mathcal H}^{2}$ -representation. It also provides a closed-form expression of the cluster bases and coupling matrices with respect to original matrix entries. The resultant ${mathcal H}^{2}$ -matrix is then directly solved for electrically large scattering analysis. Numerical experiments have demonstrated the accuracy and efficiency of the proposed algorithm. In addition to surface integral equations, the proposed algorithms can also be applied to solving other electrically large integral equations.
{"title":"Nested Pseudo Skeleton Approximation Algorithm for Generating ${mathcal H}^{2}$-Matrix Representations of Electrically Large Surface Integral Equations","authors":"Chang Yang;Dan Jiao","doi":"10.1109/JMMCT.2024.3487779","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3487779","url":null,"abstract":"In this paper, we develop a kernel-independent and purely algebraic method, Nested Pseudo-Skeleton Approximation (NPSA) algorithm, to generate a low-rank \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-matrix representation of electrically large surface integral equations (SIEs). The algorithm only uses \u0000<inline-formula><tex-math>$O(NlogN)$</tex-math></inline-formula>\u0000 entries of the original dense SIE matrix of size \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 to generate the \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-representation. It also provides a closed-form expression of the cluster bases and coupling matrices with respect to original matrix entries. The resultant \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-matrix is then directly solved for electrically large scattering analysis. Numerical experiments have demonstrated the accuracy and efficiency of the proposed algorithm. In addition to surface integral equations, the proposed algorithms can also be applied to solving other electrically large integral equations.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"393-402"},"PeriodicalIF":1.8,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636427","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}
Pub Date : 2024-10-08DOI: 10.1109/JMMCT.2024.3475988
Mai Le;Alan Yao;Amie Zhang;Hieu Le;Zhaoyang Chen;Xuqing Wu;Lihong Zhao;Jiefu Chen
Solid-state batteries can offer enhanced safety and potentially higher energy density compared to traditional lithium-ion batteries. However, their power density remains a challenge due to limited ionic conductivity in composite electrodes caused by non-ideal microstructures. Laborious experimental processes and time-consuming data analysis algorithms are obstacles to establishing structure–performance correlations and optimizing electrode microstructure. In this paper, we present a machine learning approach to predict the effective conductivity of a composite electrode based on scanning electron microscopy images, using binary images composed of conductive and non-conductive regions and an ionic conductivity value of the conductive region. We show that our proposed method is two orders of magnitude more efficient than conventional numerical schemes such as the finite difference method.
{"title":"Expediting Ionic Conductivity Prediction of Solid-State Battery Electrodes Using Machine Learning","authors":"Mai Le;Alan Yao;Amie Zhang;Hieu Le;Zhaoyang Chen;Xuqing Wu;Lihong Zhao;Jiefu Chen","doi":"10.1109/JMMCT.2024.3475988","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3475988","url":null,"abstract":"Solid-state batteries can offer enhanced safety and potentially higher energy density compared to traditional lithium-ion batteries. However, their power density remains a challenge due to limited ionic conductivity in composite electrodes caused by non-ideal microstructures. Laborious experimental processes and time-consuming data analysis algorithms are obstacles to establishing structure–performance correlations and optimizing electrode microstructure. In this paper, we present a machine learning approach to predict the effective conductivity of a composite electrode based on scanning electron microscopy images, using binary images composed of conductive and non-conductive regions and an ionic conductivity value of the conductive region. We show that our proposed method is two orders of magnitude more efficient than conventional numerical schemes such as the finite difference method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"375-382"},"PeriodicalIF":1.8,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517850","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}
Pub Date : 2024-09-30DOI: 10.1109/JMMCT.2024.3470557
Dhirodaatto Sarkar;Jue Wang;Alex Chortos
Finding applications in fields such as manipulation platforms and gas sensors, various strategies have been developed to enhance scale and resolution of resistive heating arrays, including integration of diodes/transistors. However, emerging applications in soft robotics and wearable devices prioritize systems that can be fabricated over large areas using low-cost materials, and benefit from simplified control. Utilizing common row/column electrodes to address heating elements, matrix addressing reduces the complexity of control inputs. Passive matrices require no semiconductor components, further minimizing device complexity. Despite these advantages, thermal and electrical crosstalk hinder passive matrix addressing. In this study, we present a novel systematic analysis of the crosstalk in passive matrix resistive heating arrays, addressing both electrical and thermal couplings. We employ theoretical and computational approaches to investigate the effects of materials and array geometry on crosstalk. Through COMSOL multiphysics simulations, we quantify crosstalk as a function of the conductivity of the constituent materials and array geometry. The computational approach allows us to decouple the effects of electrical and thermal crosstalk. Additionally, Pattern Search is used to optimize array designs, minimizing crosstalk and voltage input and revealing trade-offs at various array scales (illustrated in a 16 × 16 array). Furthermore, we study the significant impact of thermal patterns and control methods on crosstalk by implementing progressive scan. This work provides insights and optimization strategies for the design of resistive heating arrays used as actuators or sensors in soft robotics and wearable devices, highlighting its practical significance in the advancement of these emerging applications.
{"title":"Crosstalk Analysis in Passively Addressed Soft Resistive Heating Arrays","authors":"Dhirodaatto Sarkar;Jue Wang;Alex Chortos","doi":"10.1109/JMMCT.2024.3470557","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3470557","url":null,"abstract":"Finding applications in fields such as manipulation platforms and gas sensors, various strategies have been developed to enhance scale and resolution of resistive heating arrays, including integration of diodes/transistors. However, emerging applications in soft robotics and wearable devices prioritize systems that can be fabricated over large areas using low-cost materials, and benefit from simplified control. Utilizing common row/column electrodes to address heating elements, matrix addressing reduces the complexity of control inputs. Passive matrices require no semiconductor components, further minimizing device complexity. Despite these advantages, thermal and electrical crosstalk hinder passive matrix addressing. In this study, we present a novel systematic analysis of the crosstalk in passive matrix resistive heating arrays, addressing both electrical and thermal couplings. We employ theoretical and computational approaches to investigate the effects of materials and array geometry on crosstalk. Through COMSOL multiphysics simulations, we quantify crosstalk as a function of the conductivity of the constituent materials and array geometry. The computational approach allows us to decouple the effects of electrical and thermal crosstalk. Additionally, Pattern Search is used to optimize array designs, minimizing crosstalk and voltage input and revealing trade-offs at various array scales (illustrated in a 16 × 16 array). Furthermore, we study the significant impact of thermal patterns and control methods on crosstalk by implementing progressive scan. This work provides insights and optimization strategies for the design of resistive heating arrays used as actuators or sensors in soft robotics and wearable devices, highlighting its practical significance in the advancement of these emerging applications.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"353-365"},"PeriodicalIF":1.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408685","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}
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