Geostatistical Learning | Júlio Hoffimann | JuliaCon2021

This talk was presented as part of JuliaCon2021 Geostatistical Learning is a new branch of Geostatistics concerned with learning functions over geospatial domains (e.g. 2D maps, 3D subsurface models). The theory is being carefully implemented in the framework, which is an extensible framework for high-performance geostatistics in Julia. In this talk, I will illustrate how the framework can be used to learn functions over general unstructured meshes, and how this unique technology can help advance geoscientific work. The theory was introduced in our recent (open access) paper available online: