3 papers
cs.LG2025
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Pablo Flores, Olga Graf, Pavlos Protopapas +1
Physics-Informed Neural Networks (PINNs) have been widely used to obtain solutions to various physical phenomena modeled as Differential Equations. As PINNs are not naturally equip…
cs.LG2025
A self-regulated convolutional neural network for classifying variable stars
Francisco Pérez-Galarce, Jorge MartÃnez-Palomera, Karim Pichara +2
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning…
astro-ph.IM2025
Multiband Embeddings of Light Curves
I. Becker, P. Protopapas, M. Catelan +1
In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our p…