Parameterization for polynomial curve approximation via residual deep neural networks

Felix Scholz, Bert Jüttler

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Finding the optimal parameterization for fitting a given sequence of data points with a parametric curve is a challenging problem that is equivalent to solving a highly non-linear system of equations. In this work, we propose the use of a residual neural network to approximate the function that assigns to a sequence of data points a suitable parameterization for fitting a polynomial curve of a fixed degree. Our model takes as an input a small fixed number of data points and the generalization to arbitrary data sequences is obtained by performing multiple evaluations. We show that the approach compares favorably to classical methods in a number of numerical experiments that include the parameterization of polynomial as well as non-polynomial data.

本文言語English
論文番号101977
ジャーナルComputer Aided Geometric Design
85
DOI
出版ステータスPublished - 2021 2
外部発表はい

ASJC Scopus subject areas

  • Modelling and Simulation
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Graphics and Computer-Aided Design

フィンガープリント 「Parameterization for polynomial curve approximation via residual deep neural networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル