3 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
cs.LG2021
Uncertainty Quantification in Neural Differential Equations
Olga Graf, Pablo Flores, Pavlos Protopapas +1
Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge. With increased usage of deep learning in vario…
eess.SP2019
One-Bit Sigma-Delta modulation on the circle
Olga Graf, Felix Krahmer, Sara Krause-Solberg
Manifold models in data analysis and signal processing have become more prominent in recent years. In this paper, we will look at one of the main tasks of modern signal processing,…