  Rapid development in numerical modelling of materials and the complexity of
new models increases quickly together with their computational demands. Despite
the growing performance of modern computers and clusters, calibration of such
models from noisy experimental data remains a nontrivial and often
computationally exhaustive task. The layered neural networks thus represent a
robust and efficient technique to overcome the time-consuming simulations of a
calibrated model. The potential of neural networks consists in simple
implementation and high versatility in approximating nonlinear relationships.
Therefore, there were several approaches proposed to accelerate the calibration
of nonlinear models by neural networks. This contribution reviews and compares
three possible strategies based on approximating (i) model response, (ii)
inverse relationship between the model response and its parameters and (iii)
error function quantifying how well the model fits the data. The advantages and
drawbacks of particular strategies are demonstrated on the calibration of four
parameters of the affinity hydration model from simulated data as well as from
experimental measurements. This model is highly nonlinear, but computationally
cheap thus allowing its calibration without any approximation and better
quantification of results obtained by the examined calibration strategies. The
paper can be thus viewed as a guide intended for the engineers to help them
select an appropriate strategy in their particular calibration problems.
