2 papers
physics.flu-dyn2025
An approximate Riemann solver approach in Physics-Informed Neural Networks for hyperbolic conservation laws
Jorge F. Urbán, José A. Pons
This study enhances the application of Physics-Informed Neural Networks (PINNs) for modeling discontinuous solutions in both hydrodynamics and relativistic hydrodynamics. Conventio…
physics.comp-ph2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
Jorge F. Urbán, Petros Stefanou, José A. Pons
This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and traditional numerical meth…