Antonio Ferrer-Sánchez
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View article: Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era
Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era Open
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost…
View article: Efficient ML-Assisted Particle Track Reconstruction Designs
Efficient ML-Assisted Particle Track Reconstruction Designs Open
Track reconstruction is a crucial part of High Energy Physics experiments. Traditional methods for the task, relying on Kalman Filters, scale poorly with detector occupancy. In the context of the upcoming High Luminosity-LHC, solutions bas…
View article: Physics-informed neural networks for an optimal counterdiabatic quantum computation
Physics-informed neural networks for an optimal counterdiabatic quantum computation Open
A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspire…
View article: Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics Open
View article: Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation Open
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 circuits comprised of systems with $N_{Q}$ qubits. The pri…
View article: Gradient-Annihilated PINNs for Solving Riemann Problems: Application to Relativistic Hydrodynamics
Gradient-Annihilated PINNs for Solving Riemann Problems: Application to Relativistic Hydrodynamics Open
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces …
View article: Gradient-Annihilated Pinns for Solving Riemann Problems: Application To Relativistic Hydrodynamics
Gradient-Annihilated Pinns for Solving Riemann Problems: Application To Relativistic Hydrodynamics Open
View article: Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning
Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning Open