4 citations · 7 across the 5 of their papers we have counts for
5 papers
Kerr Quasinormal Modes without Variable Separation: A Two-Dimensional Hyperboloidal Teukolsky Solver with Physics-Informed Neural Networks
Antonio Ferrer-Sánchez, Daniela D. Doneva, José D. Martín-Guerrero +4
We use physics-informed neural networks (PINNs) to solve the gravitational quasinormal-mode (QNM) eigenvalue problem for Kerr spacetime directly in the two-dimensional hyperboloida…
Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems
Antonio Ferrer-Sánchez, Yolanda Vives-Gilabert, Yue Ban +2
Quantum Fisher Information (QFI) sets the ultimate precision limit for parameter estimation and is therefore a central quantity in quantum metrology. In time-dependent many-body sy…
Addressing the gravitational collapse of a massless scalar field with Physics-Informed Neural Networks
Antonio Ferrer-Sánchez, Nino Villanueva-Espinosa, Carlos Hernani Morales +4
The gravitational collapse of a massless scalar field remains a demanding benchmark for numerical methods in numerical relativity, as it exhibits critical behavior at the boundary…
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales +7
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum…
Gradient-Annihilated PINNs for Solving Riemann Problems: Application to Relativistic Hydrodynamics
Antonio Ferrer-Sánchez, José D. Martín-Guerrero, Roberto Ruiz de Austri +2
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method…