Discovering and forecasting extreme events via active learning in neural operators. (arXiv:2204.02488v1 [cs.LG])

Extreme events in society and nature, such as pandemic spikes or rogue waves,
can have catastrophic consequences. Characterizing extremes is difficult as
they occur rarely, arise from seemingly benign conditions, and belong to
complex and often unknown infinite-dimensional systems. Such challenges render
attempts at characterizing them as moot. We address each of these difficulties
by combining novel training schemes in Bayesian experimental design (BED) with
an ensemble of deep neural operators (DNOs). This model-agnostic framework
pairs a BED scheme that …

active learning arxiv events forecasting learning operators