Ranjan Anantharaman

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.

Lux.jl: Explicit Parameterization of Neural Networks in Julia

Adaptive Radial Basis Function Surrogates in Julia

In this talk, we will present the JuliaCon proceedings, the purpose, scope, and target audience of this venue. The proceedings are a community-driven initiative to publish articles of interest to the research and developer communities gathered by JuliaCon, they do not require application processing fees nor a paywall on article, making both producing and accessing the articles possible for all. We will then give a quick tour of the reviewing and publication process which happen transparently in

Ranjan Anantharaman

Talks:

Adaptive Radial Basis Function Surrogates in Julia

The JuliaCon Proceedings

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