50,072 research outputs found
A Fast and Accurate Algorithm for Spherical Harmonic Analysis on HEALPix Grids with Applications to the Cosmic Microwave Background Radiation
The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used
extensively in astrophysics for data collection and analysis on the sphere. The
scheme was originally designed for studying the Cosmic Microwave Background
(CMB) radiation, which represents the first light to travel during the early
stages of the universe's development and gives the strongest evidence for the
Big Bang theory to date. Refined analysis of the CMB angular power spectrum can
lead to revolutionary developments in understanding the nature of dark matter
and dark energy. In this paper, we present a new method for performing
spherical harmonic analysis for HEALPix data, which is a central component to
computing and analyzing the angular power spectrum of the massive CMB data
sets. The method uses a novel combination of a non-uniform fast Fourier
transform, the double Fourier sphere method, and Slevinsky's fast spherical
harmonic transform (Slevinsky, 2019). For a HEALPix grid with pixels
(points), the computational complexity of the method is , with an initial set-up cost of . This compares
favorably with runtime complexity of the current methods
available in the HEALPix software when multiple maps need to be analyzed at the
same time. Using numerical experiments, we demonstrate that the new method also
appears to provide better accuracy over the entire angular power spectrum of
synthetic data when compared to the current methods, with a convergence rate at
least two times higher
Interpolation-based parameterized model order reduction of delayed systems
Three-dimensional electromagnetic methods are fundamental tools for the analysis and design of high-speed systems. These methods often generate large systems of equations, and model order reduction (MOR) methods are used to reduce such a high complexity. When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. Design space optimization and exploration are usually performed during a typical design process that consequently requires repeated simulations for different design parameter values. Efficient performing of these design activities calls for parameterized model order reduction (PMOR) methods, which are able to reduce large systems of equations with respect to frequency and other design parameters of the circuit, such as layout or substrate features. We propose a novel PMOR method for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, a procedure to find scaling and frequency shifting coefficients and positive interpolation schemes. The proposed scaling and frequency shifting coefficients enhance and improve the modeling capability of standard positive interpolation schemes and allow accurate modeling of highly dynamic systems with a limited amount of initial univariate models in the design space. The proposed method is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach
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