Efficient parallel LOD-FDTD method for Debye-dispersive media

The locally one-dimensional finite-difference time-domain (LOD-FDTD) method is a promising implicit technique for solving Maxwell’s equations in numerical electromagnetics. Thispaper describes an efficient message passing interface (MPI)-parallel implementation of the LOD-FDTD method for Debye-dispersive media. Its computational efficiency is demonstrated to be superior to that of the parallel ADI-FDTD method. We demonstrate the effectiveness of the proposed parallel algorithm in the simulation of a bio-electromagnetic problem: the deep brain stimulation (DBS) in the human body.The work described in this paper and the research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013, under grant agreement no 205294 (HIRF SE project), and from the Spanish National Projects TEC2010-20841-C04-04, CSD2008-00068, and the Junta de Andalucia Project P09-TIC-5327

Stereolithographic biomodeling of equine ovary based on 3D serial digitizing device

The 3D internal structure microscopy (3D-ISM) was applied to the equine ovary, which possesses peculiar structural characteristics. Stereolithography was applied to make a life-sized model by means of data obtained from 3D-ISM. Images from serially sliced surfaces contributed to a successful 3D reconstruction of the equine ovary. Photopolymerized resin models of equine ovaries produced by stereolithography can clearly show the internal structure and spatial localizations in the ovary. The understanding of the spatial relationship between the ovulation fossa and follicles and/or corpora lutea in the equine ovary was a great benefit. The peculiar structure of the equine ovary could be thoroughly observed and understood through this model

A statistical parsimony method for uncertainty quantification of FDTD computation based on the PCA and ridge regression

International audienc

Uncertainty analysis on FDTD computation with artificial neural network

International audienc

Development of ultra-fine-grain binderless cBN tool for precision cutting of ferrous materials

A new cutting tool was developed from ultra-fine-grain (<100 nm), binderless cubic boron nitride (cBN) material fabricated by transforming hexagonal boron nitride to cBN by means of sintering under an ultra-high pressure of 10 GPa at 1800℃. The cutting edges of the newly developed cBN tool can be made as sharp as those of single-crystal diamond tools. In this experiment, cBN and single-crystal diamond tools of the same shape were compared by precision cutting tests using stainless steel specimens and steel specimens coated with an electroless Ni-P layer. The surface roughness (Rz) of specimen surfaces cut with the cBN tool by means of planing was approximately 100 nm for both the Ni-P-coated steel and stainless steel specimens. Though similar Rz values were obtained for Ni-P layers cut by the cBN and diamond tools, an Rz value exceeding 2000 nm was obtained for stainless steel cut by the diamond tool. High-precision surfaces with Rz values of 50-100 nm were obtained for stainless steel specimens cut with the cBN tool under high-speed milling (942 m/min) conditions. These results indicate that the newly developed cBN tool is useful for the ultra-precision or precision cutting of ferrous materials

A general framework for building surrogate models for uncertainty quantification in computational electromagnetics

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An Adaptive Least Angle Regression Method for Uncertainty Quantification in FDTD Computation

The nonintrusive polynomial chaos expansion method is used to quantify the uncertainty of a stochastic system. It potentially reduces the number of numerical simulations in modeling process, thus improving efficiency while ensuring accuracy. However, the number of polynomial bases grows substantially with the increase of random parameters, which may render the technique ineffective due to the excessive computational resources. To address such problems, methods based on the sparse strategy such as the least angle regression (LARS) method with hyperbolic index sets can be used. This paper presents the first work to improve the accuracy of the original LARS method for uncertainty quantification. We propose an adaptive LARS method in order to quantify the uncertainty of the results from the numerical simulations with higher accuracy than the original LARS method. The proposed method outperforms the original LARS method in terms of accuracy and stability. The L2 regularization scheme further reduces the number of input samples while maintaining the accuracy of the LARS method. © 2018 IEEE