1 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
A high order unfitted hybridizable discontinuous Galerkin method for linear elasticity
Juan M. Cardenas, Manuel Solano
This work analyzes a high order hybridizable discontinuous Galerkin (HDG) method for the linear elasticity problem in a domain not necessarily polyhedral. The domain is approximate…
Towards optimal sampling for learning sparse approximation in high dimensions
Ben Adcock, Juan M. Cardenas, Nick Dexter +1
In this chapter, we discuss recent work on learning sparse approximations to high-dimensional functions on data, where the target functions may be scalar-, vector- or even Hilbert…
An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains
Ben Adcock, Juan M. Cardenas, Nick Dexter
Many problems in computational science and engineering can be described in terms of approximating a smooth function of variables, defined over an unknown domain of interest $Ω\…
Near-optimal sampling strategies for multivariate function approximation on general domains
Ben Adcock, Juan M. Cardenas
In this paper, we address the problem of approximating a multivariate function defined on a general domain in dimensions from sample points. We consider weighted least-squares…