71 citations · 86 across the 4 of their papers we have counts for
5 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…
Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
Elham Kiyani, Khemraj Shukla, Jorge F. Urbán +2
Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network's trainin…
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…