Adaptive Radial Basis Function Surrogates in Julia

This talk focuses on an iterative algorithm, called active learning, to update radial basis function surrogates by adaptively choosing points across its input space. This work extensively uses the SciML ecosystem, and in particular, Surrogates.jl.

Active Learning algorithms have been applied to fine tune surrogate models. In this talk, we analyze these algorithms in the context of dynamical systems with a large number of input parameters. The talk will demonstrate:

1. An adaptive learning algorithm for radial basis functions
2. Its efficacy on dynamical systems with high dimensional input parameter spaces

This will make use of Surrogates.jl and the rest of the SciML ecosystem.