Web: http://arxiv.org/abs/2204.02488

Sept. 21, 2022, 1:11 a.m. | Ethan Pickering, Stephen Guth, George Em Karniadakis, Themistoklis P. Sapsis

stat.ML updates on arXiv.org arxiv.org

Extreme events in society and nature, such as pandemic spikes, rogue waves,
or structural failures, 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 …

active learning arxiv events forecasting operators

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