@@ -78,6 +78,8 @@ Most of our algorithms allow for a customizable loss function with which one can
It integrates with the Jupyter notebook environment as well as popular parallel computation frameworks such as `ipyparallel`, `mpi4py`, and `dask.distributed`.
It provides auxiliary functionality such as live-plotting, inspecting the data as the calculation is in progress, and automatically saving and loading of the data.
The raw data and source code that produces all plots in this paper is available at [@papercode].
# Review of adaptive sampling
Optimal sampling and planning based on data is a mature field with different communities providing their own context, restrictions, and algorithms to solve their problems.
