3 papers
cs.LG2026
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…
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…