449 citations · 462 across the 3 of their papers we have counts for
5 papers
Spectrum and Physics-Informed Neural Networks (SaPINNs) for Input-State-Parameter Estimation in Dynamic Systems Subjected to Natural Hazards-Induced Excitation
Antonina Kosikova, Apostolos Psaros, Andrew Smyth
System identification under unknown external excitation is an inherently ill-posed problem, typically requiring additional knowledge or simplifying assumptions to enable reliable s…
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
Qianli Shen, Wai Hoh Tang, Zhun Deng +2
Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations. Indicatively, strong assumptions regarding the data l…
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…
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…
Meta-learning PINN loss functions
Apostolos F Psaros, Kenji Kawaguchi, George Em Karniadakis
We propose a meta-learning technique for offline discovery of physics-informed neural network (PINN) loss functions. We extend earlier works on meta-learning, and develop a gradien…