  Physics-Informed Neural Networks (PINNs) have gained much attention in
various fields of engineering thanks to their capability of incorporating
physical laws into the models. However, the assessment of PINNs in industrial
applications involving coupling between mechanical and thermal fields is still
an active research topic. In this work, we present an application of PINNs to a
non-Newtonian fluid thermo-mechanical problem which is often considered in the
rubber calendering process. We demonstrate the effectiveness of PINNs when
dealing with inverse and ill-posed problems, which are impractical to be solved
by classical numerical discretization methods. We study the impact of the
placement of the sensors and the distribution of unsupervised points on the
performance of PINNs in a problem of inferring hidden physical fields from some
partial data. We also investigate the capability of PINNs to identify unknown
physical parameters from the measurements captured by sensors. The effect of
noisy measurements is also considered throughout this work. The results of this
paper demonstrate that in the problem of identification, PINNs can successfully
estimate the unknown parameters using only the measurements on the sensors. In
ill-posed problems where boundary conditions are not completely defined, even
though the placement of the sensors and the distribution of unsupervised points
have a great impact on PINNs performance, we show that the algorithm is able to
infer the hidden physics from local measurements.
