2 citations · 2 across the 2 of their papers we have counts for
7 papers
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino +2
Physics-Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains…
Stability of nonlinear dissipative systems with applications in fluid dynamics
Javier Gonzalez-Conde, Daniel Isla, Sergiy Zhuk +1
Nonlinear partial differential equations are central to physics, engineering, and finance. Except in a limited number of integrable cases, their solution generally requires numeric…
Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino +3
Scientific Machine Learning (SciML) integrates physics and data into the learning process, offering improved generalization compared with purely data-driven models. Despite its pot…
Existence of unbiased resilient estimators in discrete quantum systems
Javier Navarro, Ricard Ravell RodrÃguez, Mikel Sanz
The Cramér-Rao bound serves as a crucial lower limit for the mean squared error of an estimator in frequentist parameter estimation. Paradoxically, it requires highly accurate pri…
Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation
Maximilian Reichert, Quntao Zhuang, Mikel Sanz
We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless…
Quantum Carleman linearisation efficiency in nonlinear fluid dynamics
Javier Gonzalez-Conde, Dylan Lewis, Sachin S. Bharadwaj +1
Computational fluid dynamics (CFD) is a specialised branch of fluid mechanics that utilises numerical methods and algorithms to solve and analyze fluid-flow problems. One promising…