Machine-Learning-Based PML For The FDTD Method

In this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark the performance of the proposed method. The results demonstrate that the newly proposed method could replace conventional PML and could be integrated into FDTD solving process with satisfactory accuracy and compatibility to FDTD. According to our knowledge, this proposed model combined artificial neural network (ANN) model is an unreported new approach based on a machine learning based for FDTD

Machine Learning Based Neural Network Solving Methods For The FDTD Method

In this paper, two novel computational processes are proposed to solve Finite-Difference Time-Domain (FDTD) based on machine learning deep neural networks. The field and boundary conditions are employed to establish recurrent neural network FDTD (RNN-FDTD) model and convolution neural network FDTD (CNN-FDTD) model respectively. Numerical examples from scalar wave equations are provided to benchmark the performance of the proposed methods. The results demonstrate that the newly proposed methods could solve FDTD steps with satisfactory accuracy. According to our knowledge, these are unreported new approaches for machine learning based FDTD solving methods

Fracture mode control: a bio-inspired strategy to combat catastrophic damage

The excellent mechanical properties of natural biomaterials have attracted intense attention from researchers with focus on the strengthening and toughening mechanisms. Nevertheless, no material is unconquerable under sufficiently high load. If fracture is unavoidable, constraining the damage scope turns to be a practical way to preserve the integrity of the whole structure. Recent studies on biomaterials have revealed that many structural biomaterials tend to be fractured, under sufficiently high indentation load, through ring cracking which is more localized and hence less destructive compared to the radial one. Inspired by this observation, here we explore the factors affecting the fracture mode of structural biomaterials idealized as laminated materials. Our results suggest that fracture mode of laminated materials depends on the coating/substrate modulus mismatch and the indenter size. A map of fracture mode is developed, showing a critical modulus mismatch (CMM), below which ring cracking dominates irrespective of the indenter size. Many structural biomaterials in nature are found to have modulus mismatch close to the CMM. Our results not only shed light on the mechanics of inclination to ring cracking exhibited by structural biomaterials but are of great value to the design of laminated structures with better persistence of structural integrity.Research Grants Council (Hong Kong, China). Early Career Scheme (Grant 533312)Hong Kong Polytechnic University. Departmental General Research Funds (Internal Competitive Research Grants 4-ZZA8)Hong Kong Polytechnic University. Departmental General Research Funds (Internal Competitive Research Grants A-PM24)Hong Kong Polytechnic University. Departmental General Research Funds (Internal Competitive Research Grants G-UA20

Magic-angle Twisted Bilayer Systems with Quadratic-Band-Touching: Exactly Flat Bands with High-Chern Number

Studies of twisted morie systems have been mainly focused on 2D materials like graphene with Dirac points and transition-metal-dichalcogenide so far. Here we propose a new twisted bilayer of 2D systems which feature quadratic-band-touching points and find exotic physics different from previously studied twisted morie systems. Specifically, we show that exactly flat bands can emerge at magic angles and, more interestingly, each flat band exhibits a high Chern number ($C=\pm 2$) which was not realized in bilayer morie systems before. We further consider the effect of Coulomb interactions in such magic-angle twisted system and find that the ground state supports the quantum anomalous Hall effect with quantized Hall conductivity $2\frac{e^2}{hc}$ at certain filling. Furthermore, possible physical realization of such twisted bilayer systems will be briefly discussed.Comment: 4.6 pages + references + supplemental, 4 figure

Machine Learning Based MoM (ML-MoM) For Parasitic Capacitance Extractions

This paper is a rethinking of the conventional method of moments (MoM) using the modern machine learning (ML) technology. By repositioning the MoM matrix and unknowns in an artificial neural network (ANN), the conventional linear algebra MoM solving is changed into a machine learning training process. The trained result is the solution. As an application, the parasitic capacitance extraction broadly needed by VLSI modeling is solved through the proposed new machine learning based method of moments (ML-MoM). The multiple linear regression (MLR) is employed to train the model. The computations are done on Amazon Web Service (AWS). Benchmarks demonstrated the interesting feasibility and efficiency of the proposed approach. According to our knowledge, this is the first MoM truly powered by machine learning methods. It opens enormous software and hardware resources for MoM and related algorithms that can be applied to signal integrity and power integrity simulations

Chapter Tradutor como mediador cultural. A tradução de intertextos e expressões idiomáticas em Subtilezas e crueldade da cozinha chinesa de Maria Ondina Braga

Yao Jing Ming’s article deals with a deep reflection on translation problems from Chinese to Portuguese languages, analysing some specifical examples of idiomatic expressions and intertextual aspects concerning a Maria Ondina Braga’s chronicle which has been translated in Chinese language by He Meng in this article

Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder-Decoder Structure

This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green’s functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder–decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received in the measurement domain are utilized as the input for the encoder to extract feature fragments, the final output for the counterpart decoder is the contrasts (permittivities) of dielectric scatterers in the target domain. In this way, unlike the conventional methods, the unknown domain between the target domain and measurement domain never has to be considered to compute inhomogeneous Green’s functions. Consequently, the inhomogeneous EMIS problems could be solved with higher accuracy even for extremely high-contrast targets. Numerical examples illustrate the feasibility of the proposed DL approach. It acts as a new candidate for solving EMIS problems in inhomogeneous media

Coupling DGTD And Behavioral Macromodel For Transient Heterogeneous Electromagnetic Simulations

A novel transient field-circuit cosimulation method based on the discontinuous Galerkin time domain (DGTD) method and circuit behaviour macromodels for heterogeneous electromagnetics such as EMC problems is proposed. The traditional field-circuit simulation method needs to know the detail of the circuits, which cannot handle problems with unknown systems due to IP protections. To overcome this problem, the proposed method utilizes the artificial neural network (ANN) to create trainable behavioral macromodels of the unknown circuits based on the port currents and port voltages. The obtained macromodel is then coupled with DGTD that describes the behavior of electromagnetic subsystem to form equations of the whole system, which can be solved to obtain transient fields, voltages and currents. Numerical examples have been benchmarked to demonstrate the capability of the proposed method