Parameterization for polynomial curve approximation via residual deep neural networks

Felix Scholz*, Bert Jüttler


研究成果: Article査読

8 被引用数 (Scopus)


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.

ジャーナルComputer Aided Geometric Design
出版ステータスPublished - 2021 2月

ASJC Scopus subject areas

  • モデリングとシミュレーション
  • 自動車工学
  • 航空宇宙工学
  • コンピュータ グラフィックスおよびコンピュータ支援設計


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