Extending a physics-based constitutive model using genetic programming

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