  We describe an approach for empirical modeling of steel phase kinetics based
on symbolic regression and genetic programming. The algorithm takes processed
data gathered from dilatometer measurements and produces a system of
differential equations that models the phase kinetics. Our initial results
demonstrate that the proposed approach allows to identify compact differential
equations that fit the data. The model predicts ferrite, pearlite and bainite
formation for a single steel type. Martensite is not yet included in the model.
Future work shall incorporate martensite and generalize to multiple steel types
with different chemical compositions.
