CAS4DL

This is a repository associated with the paper "" by Ben Adcock, Juan M. Cardenas, and Nick Dexter

CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning by Ben Adcock, Juan M. Cardenas and Nick Dexter.

to be published by SAMPTA in late 2022, available at https://arxiv.org/abs/2208.12190

If you have questions or comments about the code, please contact ben_adcock@sfu.ca, jcardena@sfu.ca, nicholas_dexter@sfu.ca.

Parts of this repository are based on the code for the paper "The gap between theory and practice in function approximation with deep neural networks" by Ben Adcock and Nick Dexter, which is available in the repository https://github.com/ndexter/MLFA

Code organization

Files are organized into four main directories:

src

Contains the main Matlab files used to create figures

Organized in Figures

utils

Contains various Matlab functions needed across main scripts

data

Contains .mat files generated by scripts in src

Organized in Figures

Filenames

These generaically take the form

fig_[number]_[row]_[col]

where [number] is the figure numbers and [row] and [col] are the row number and column number is multi-panel figures.