2 papers
cs.LG2024
GrINd: Grid Interpolation Network for Scattered Observations
Andrzej Dulny, Paul Heinisch, Andreas Hotho +1
Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional me…
cs.LG2023
TaylorPDENet: Learning PDEs from non-grid Data
Paul Heinisch, Andrzej Dulny, Anna Krause +1
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to…