skip to content
 

Polynomial approximation of high-dimensional functions on irregular domains

Presented by: 
Ben Adcock Simon Fraser University
Date: 
Thursday 8th February 2018 - 09:00 to 10:00
Venue: 
INI Seminar Room 1
Abstract: 
Co-author: Daan Huybrechs (KU Leuven)

Smooth, multivariate functions defined on tensor domains can be approximated using orthonormal bases formed as tensor products of one-dimensional orthogonal polynomials. On the other hand, constructing orthogonal polynomials in irregular domains is difficult and computationally intensive. Yet irregular domains arise in many applications, including uncertainty quantification, model-order reduction, optimal control and numerical PDEs. In this talk I will introduce a framework for approximating smooth, multivariate functions on irregular domains, known as polynomial frame approximation. Importantly, this approach corresponds to approximation in a frame, rather than a basis; a fact which leads to several key differences, both theoretical and numerical in nature. However, this approach requires no orthogonalization or parametrization of the domain boundary, thus making it suitable for very general domains, including a priori unknown domains. I will discuss theoretical result s for the approximation error, stability and sample complexity of this approach, and show its suitability for high-dimensional approximation through independence (or weak dependence) of the guarantees on the ambient dimension d. I will also present several numerical results, and highlight some open problems and challenges.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons