51 citations · 507 across the 30 of their papers we have counts for
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cs.LG2022★ 3 cited
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…
astro-ph.SR2022★ 10 cited
Semi-Supervised Classification and Clustering Analysis for Variable Stars
R. Pantoja, M. Catelan, K. Pichara +1
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sou…