Generalized DeepONets for Viscosity Prediction Using Learned Entropy Scaling References

In the present work, the well-established entropy scaling approach is incorporated into a neural network architecture to predict the shear viscosity of a diverse set of pure fluids over a large temperature and pressure range. Instead of imposing a particular form of the reference entropy and reference shear viscosity, these properties are learned.

The resulting architecture can be interpreted as two linked DeepONets with generalization capabilities.