Spectral Method Semantic Scholar

Spectral Method Semantic Scholar Spectral methods are a class of techniques used in applied mathematics and scientific computing to numerically solve certain differential equations, often involving the use of the fast fourier transform. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.

Semantic Scholar Product This monograph aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large scale applications. Semantic scholar uses groundbreaking ai and engineering to understand the semantics of scientific literature to help scholars discover relevant research. This book provides a detailed presentation of basic spectral algorithms, as well as a systematical presentation of basic convergence theory and error analysis for spectral methods. To address this bottleneck, we develop a novel synthesis that combines a high order spectral method for spatial discretization with a fast memory algorithm based on a sum of exponentials approximation. the spectral method obtains exponential spatial convergence for smooth solutions.

Semantic Scholar Webcurate This book provides a detailed presentation of basic spectral algorithms, as well as a systematical presentation of basic convergence theory and error analysis for spectral methods. To address this bottleneck, we develop a novel synthesis that combines a high order spectral method for spatial discretization with a fast memory algorithm based on a sum of exponentials approximation. the spectral method obtains exponential spatial convergence for smooth solutions. This monograph aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large scale applications. In this paper, we develop an efficient fourier legendre spectral galerkin method for solving elliptic partial differential equations on general two dimensional domains. a key core of our approach is employing a harmonic map to handle the general physical domains. this technique ensures broad geometric applicability, making the method highly effective for both complex star shaped and nonstar. In this paper we introduced a method for unsupervised localization, segmentation and matting based on spectral graph theory and deep features. despite the simple for mulation, it achieves state of the art unsupervised perfor mance for these tasks. In this paper, we introduce a legendre spectral method that integrates domain decomposition and mapping techniques to address the fokker planck equation on two dimensional irregular domains.… expand.