2 papers
physics.comp-ph2025
Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen +2
Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineering. Physics-informed neural networks (…
cs.LG2025
EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Models
Håkon Hanisch Kjærnli, Lluis Mas-Ribas, Hans Jakob Håland +4
When deploying time series forecasting models based on machine learning to real world settings, one often encounter situations where the data distribution drifts. Such drifts expos…