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Multiple scattering of parallel cylinders with arbitrary cross section is computed using the T-matrix of each single scatterer and the general translational matrix for cylindrical waves. Usually, the recommended golden rule to compute the translational matrix is Graf’s addition theorem. However, this approach cannot be properly implemented for some geometries, such as in a two-cylinder case when the center of one of them falls within the minimum circular cylinder that circumscribes the other one. In order to overcome this limitation, a transformation between cylindrical waves and plane waves, followed by propagation of the latter, is proposed. The new approach succeeds due to an adequate truncation of the evanescent plane wave spectrum. This strategy is demonstrated by studying the scattering of three infinite elliptic metallic cylinders for different electrical sizes and observing the convergence of the results as a function of the truncated spectrum. Finally, to conclusively show the interest and applicability of the approach, two more complex problems are treated: a group of infinite elliptic metallic cylinders where two different sizes are combined and a practical real-life filter in substrate integrated waveguide (SIW) technology, including several groups of rectangular dielectric cylinders. [ABSTRACT FROM AUTHOR]

In this article, a unified learning-based approach is introduced to solve inverse scattering problems (ISPs) with mixed boundary conditions (BCs). The scattering behavior of hybrid dielectric and perfect electric conductors (PEC) scatterers is modeled by the T-matrix method. A rough image of the zero-order T-matrix coefficients for unknown scatterers is first reconstructed by the backpropagation (BP) method, which is then refined by an attention-assisted pix2pix generative adversarial network (GAN). The spatial attention mechanism is utilized to enforce the generator network to learn salient features of the unknown scatterers instead of the background. The adversarial training of the generator and the discriminator further enables the reconstructed image to be constrained by high-level features of reference scatterers. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed method for ISP reconstructions with hybrid scatterers. It effectively expands the application scope of learning-based ISP methods to reconstruct scatterers without knowing the BCs of scatterers in advance. [ABSTRACT FROM AUTHOR]

A neural network (NN) model is trained with a database widely used in the aerosol remote sensing community to rapidly predict the single‐scattering optical properties of spheroidal dust particles. Analytical solutions for their Jacobians with respect to microphysical properties are derived based on the functional form of the NN. The Jacobian predictions are improved by adding Jacobians from a linearized T‐matrix model into the training. Out‐of‐database testing implies that NN‐based predictions perform better than the business‐as‐usual method that interpolates optical properties from the database. Independent validation further demonstrates the efficacy of the NN‐based predictions by reducing computational costs while maintaining accuracy. This work represents the first use of machine learning‐based function approximation to computationally expedite the application of the existing spheroidal dust properties database; the resultant NN model can be implemented in atmospheric models and satellite retrieval algorithms with high accuracy, computational efficiency, and the rigor of analytical solutions. Plain Language Summary: Dust particles affect both solar and terrestrial radiative transfer, but whether they cool or warm the climate is currently an open question in the literature. Accurate estimation of dust scattering and absorption properties, while critical for climate studies, is hindered by the fact that dust particles have irregular shapes and large size ranges; hence, no single method can be applied for all particle sizes and shapes. Often, a comprehensive look‐up table of these properties is created by combining multiple methods. The application of such databases, however, is cumbersome and inaccurate due to the need for multi‐variable interpolation. Furthermore, the look‐up‐table approach lacks the mathematical rigor needed to determine the sensitivity (Jacobians) of the single‐scattering properties to the dust size, shape, and refractive index that are needed in remote sensing algorithms. The aforementioned challenges are tackled here by developing a novel approach within the neural network (NN) framework. This NN‐based approach is fast, accurate, and able to predict Jacobians with analytical formulas. The NN model can be readily applied to the dust retrieval algorithm and radiative forcing modeling. The concept of deriving Jacobians from the NN model in this study can also be generalized for application to other problems involving gradient calculations. Key Points: A neural network (NN) model is trained to characterize the optical properties of spheroidal dust particlesAnalytical Jacobians of the optical properties with respect to microphysical parameters are derived from NN model directlyAdding analytical Jacobians from a linearized T‐matrix model in the training improves the NN‐derived Jacobians [ABSTRACT FROM AUTHOR]

Hybrid computational schemes combining the advantages of a method of moments formulation of a field integral equation and T-matrix method are developed in this article. The hybrid methods are particularly efficient when describing the interaction of electrically small complex objects and electrically large objects of canonical shapes such as spherical multilayered bodies where the T-matrix method is reduced to the Mie series making the method an interesting alternative in the design of implantable antennas or exposure evaluations. Method performance is tested on a spherical multilayer model of the human head. Along with the hybrid method, an evaluation of the transition matrix of an arbitrarily shaped object is presented and the characteristic mode decomposition is performed, exhibiting fourfold numerical precision compared to conventional approaches. [ABSTRACT FROM AUTHOR]

In the mixed boundary inverse scattering problem (ISP), conducting and dielectric scatterers coexist in the same region, which challenges the present quantitative inverse scattering methods. Moreover, to ensure the incident waves penetrating the lossy or high-contrast objects, lower wavelength is applied in most inverse scattering applications. Therefore, methods with wavelength or subwavelength resolution are required for the mixed boundary ISP. In this article, we devise a quantitative inversion scheme alternately updating the contrast of dielectric scatterers and the T-matrix of conducting scatterers. The proposed alternate parameter updating method (APUM) avoids the reconstruction deterioration from both the large imaginary parts of conducting contrasts and the limited expansion order of the T-matrix. Then, we further improve the resolution of the APUM by optimizing the incident fields, which is also a regularization strategy. In particular, we design superoscillatory incident fields to quantitatively converse the high spatial spectrum of objects into low spectrum contrast sources, which can retain the high-frequency information of objects in the low-passband of the Green function. The results with synthetic data and single-frequency Fresnel experimental data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A software for the simulation of light scattering by multiple particles near planar interfaces is presented. • The usage of the software is illustrated by several application examples. • The accuracy of the results is verified by comparison to FEM and FDTD results. • Simulations with several thousand wavelength-scale particles are feasible. SMUTHI is a python package for the efficient and accurate simulation of electromagnetic scattering by one or multiple wavelength-scale objects in a planarly layered medium. The software combines the T-matrix method for individual particle scattering with the scattering matrix formalism for the propagation of the electromagnetic field through the planar interfaces. In this article, we briefly introduce the relevant theoretical concepts and present the main features of SMUTHI. Simulation results obtained for several benchmark configurations are validated against commercial software solutions. Owing to the generality of planarly layered geometries and the availability of different particle shapes and light sources, possible applications of SMUTHI include the study of discrete random media, meta-surfaces, photonic crystals and glasses, perforated membranes and plasmonic systems, to name a few relevant examples at visible and near-visible wavelengths. [ABSTRACT FROM AUTHOR]