71 citations · 86 across the 3 of their papers we have counts for
4 papers
Towards solving General Relativity with Physics-Informed Neural Networks
Juan A. Carretero, Jorge F. Urbán, Fernando Abalos +4
Physics-Informed Neural Networks (PINNs) are a machine-learning framework for approximating solutions to systems of partial differential equations by constraining neural networks t…
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…
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…
Solving the Pulsar Equation using Physics-Informed Neural Networks
Petros Stefanou, Jorge F. Urbán, José A. Pons
In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied to explore a diverse range of pulsar magneto-spheric models, specifically focusing on axisymmetric cas…