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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.

Physics-based Machine Learning

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Do you have a problem we could help solve? Let's get in touch to see if we could help you.