Genetic Programming as an explainable AI Framework

Explainability of the results of AI algorithms is an important feature especially once it comes to the applications with strong physical laws. We have developed a Genetic Programming (GP) framework for the context of fluid dynamics. The first results look very promising and the results are explainable. 


Figure: Main loop of GP, we evolve symbolic functions which fit data from simulations. 

More detilas to be presented at EUROGEN 2021 and can be found in this paper: 

  • Heiner Zille, Fabien Evrard, Julia Reuter, Sanaz Mostaghim and Berend van Wachem
  • Assessment of Multi-objective and Coevolutionary Genetic Programming for predicting the Stokes Flow around a Sphere
  • Accepted at International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control (EUROGEN 2021), ECCOMAS, June 2021

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