Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design
Probabilistic predictions with Fourier neural operators
Christopher Bülte · Philipp Scholl · Gitta Kutyniok
Keywords: [ Deep Learning ] [ neural operators ] [ Dynamical Systems ] [ uncertainty quantification ]
Neural networks have been successfully applied in modeling partial differential equations, especially in dynamical systems. Commonly used models, such as neural operators, are performing well at deterministic prediction tasks, but lack a quantification of the uncertainty inherent in many complex systems, for example weather forecasting. In this paper, we explore a new approach that combines Fourier neural operators with generative modeling based on proper scoring rules in order to create well-calibrated probabilistic predictions of dynamical systems. We demonstrate improved predictive uncertainty for our approach, especially in settings with very high inherent uncertainty.