2 citations · 2 across the 5 of their papers we have counts for
5 papers
PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
Andrea Bonfanti, Ismael Medina, Roman List +3
Recent advances in Scientific Machine Learning have shown that second-order methods can enhance the training of Physics-Informed Neural Networks (PINNs), making them a suitable alt…
A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers
Alan Rosales-Romero, Adolfo Vázquez-Quesada, Marco Ellero +1
Viscoelastic fluids play a critical role in various engineering and biological applications, where their lubrication properties are strongly influenced by relaxation times ranging…
Integral fractional viscoelastic models in SPH: LAOS simulations versus experimental data
Luca Santelli, Adolfo Vázquez-Quesada, Aizeti Burgoa +3
The rheological behaviour of a polymer was investigated by performing numerical simulations in complex flow and comparing them to experiments. For our simulations, we employed a Sm…
A GENERIC-guided active learning SPH method for viscoelastic fluids using Gaussian process regression
Xuekai Dong, David Nieto Simavilla, Jie Ouyang +2
When applying machine learning methods to learn viscoelastic constitutive relations, the polymer history dependence in viscoelastic fluids and the generalization ability of machine…
Hammering at the entropy: A GENERIC-guided approach to learning polymeric rheological constitutive equations using PINNs
David Nieto Simavilla, Andrea Bonfanti, Imanol García de Beristain +2
We present a versatile framework that employs Physics-Informed Neural Networks (PINNs) to discover the entropic contribution that leads to the constitutive equation for the extra-s…