51 citations · 507 across the 30 of their papers we have counts for
30 papers
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…
A self-regulated convolutional neural network for classifying variable stars
Francisco Pérez-Galarce, Jorge Martínez-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…
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…
Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
Helem Salinas, Karim Pichara, Rafael Brahm +2
Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematicall…
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift
Francisco Pérez-Galarce, Karim Pichara, Pablo Huijse +2
In recent decades, machine learning has provided valuable models and algorithms for processing and extracting knowledge from time-series surveys. Different classifiers have been pr…
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks
Olga Graf, Pablo Flores, Pavlos Protopapas +1
Physics-Informed Neural Networks (PINNs) are gaining popularity as a method for solving differential equations. While being more feasible in some contexts than the classical numeri…