From machine-learned preconditioners to control-oriented models

A good understanding of your problem is essential when applying machine learning tools. We know this, because our team has developed cutting-edge tools, such as

• Data-driven preconditioners (PreconNet), applied to plasma simulations but applicable in a wider context
• Surrogate models for tokamak turbulence (QLKNN)
• Machine-learned solver selection for Finite Element (FEM) codes.
• Surrogate models for solution prediction in Finite Element codes.