  In material science, models are derived to predict emergent material
properties (e.g. elasticity, strength, conductivity) and their relations to
processing conditions. A major drawback is the calibration of model parameters
that depend on processing conditions. Currently, these parameters must be
optimized to fit measured data since their relations to processing conditions
(e.g. deformation temperature, strain rate) are not fully understood. We
present a new approach that identifies the functional dependency of calibration
parameters from processing conditions based on genetic programming. We propose
two (explicit and implicit) methods to identify these dependencies and generate
short interpretable expressions. The approach is used to extend a physics-based
constitutive model for deformation processes. This constitutive model operates
with internal material variables such as a dislocation density and contains a
number of parameters, among them three calibration parameters. The derived
expressions extend the constitutive model and replace the calibration
parameters. Thus, interpolation between various processing parameters is
enabled.
  Our results show that the implicit method is computationally more expensive
than the explicit approach but also produces significantly better results.
