  Identification and parameterisation of constitutive models can be a
challenging task in rheology. We investigate the use of Random Forest (RF)
regression to estimate viscoelastic constitutive model parameters using Large
Amplitude Oscillatory Shear (LAOS) data. Specifically, we deploy the RF to
predict constitutive model parameters using the spectra of Chebyshev
coefficients pertaining to the stress-strain-strain rate Lissajous curves. As
far as we know, this is the first time Machine Learning (ML) has been utilised
for this predictive task. We test three constitutive models: the linear and
exponential Phan-Thien-Tanner (PTT) models, and the RoLiE-Poly model. For both
PTT models, the RF estimator predicts the model parameters from the Chebyshev
spectra with high accuracy. For the RoLiE-Poly model, the RF estimator
demonstrates lower accuracy in model validation, however the predicted
parameters still accurately capture the rheological behaviour in both
oscillatory shear and oscillatory extensional flow. This is because multiple
sets of RoLiE-Poly model parameters can yield very similar rheological
responses and indicates practical identifiability issues under the investigated
conditions. This does not represent an inadequacy of the framework, but rather
a fundamental challenge in estimating model parameters using rheometric data.
Overall, the study highlights the strong potential ML has to offer for
selecting and parametrising constitutive models based on rheometry data. Our
results can help in allowing for widespread robust, modelling of viscoelastic
fluid flows.

Soft Condensed Matter