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Amir Geranmayeh

Publications and source records attributed to Amir Geranmayeh.

2 recordsLinked to original sources
arXiv2021

Machine-Learning Classification of Closed and Open Radiating Wires from Near Magnetic or Electric Field Scan Images

Sets of intelligent classifiers are applied to the near-field scan-data in order to automatically classify the shape of radiating wirings. The support vector machine, k-nearest neighbors algorithm, and Gaussian process classifications are trained using the near-field radiation pattern of diverse radiating wire configurations. Leave-one-out cross-validation is used for estimating the performance of the predictive models. The output of this research is a software package well-suited to be retrained based on any measured near-field databank to automate the identification of magnetic-type or electric-type of the radiating coupling sources.

eess.IV↗
arXiv2017

Parallel Matrix-Free Implementation of Frequency-Domain Finite Difference Methods for Cluster Computing

Full-wave 3D electromagnetic simulations of complex planar devices, multilayer interconnects, and chip packages are presented for wide-band frequency-domain analysis using the finite difference integration technique developed in the PETSc software package. Initial reordering of the index assignment to the unknowns makes the resulting system matrix diagonally dominant. The rearrangement also facilitates the decomposition of large domain into slices for passing the mesh information to different machines. Matrix-free methods are then exploited to minimize the number of element-wise multiplications and memory requirements in the construction of the system of linear equations. Besides, the recipes provide extreme ease of modifications in the kernel of the code. The applicability of different Krylov subspace solvers is investigated. The accuracy is checked through comparisons with CST MICROWAVE STUDIO transient solver results. The parallel execution of the compiled code on specific number of processors in multi-core distributed-memory architectures demonstrate high scalability of the computational algorithm.

cs.CE↗