2.5k citations · 6.9k across the 23 of their papers we have counts for
3 papers · 1 filter
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Zongren Zou, Xuhui Meng, Apostolos F Psaros +1
Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different field…
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
Kevin Linka, Amelie Schafer, Xuhui Meng +3
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze non…
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
Apostolos F Psaros, Xuhui Meng, Zongren Zou +2
Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challen…