Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines Harder, Hans Peitz, Sebastian extreme learning machines partial differential equations data-driven prediction high-dimensional systems We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance. 2024 info:eu-repo/semantics/preprint doc-type:preprint text http://purl.org/coar/resource_type/c_816b https://ris.uni-paderborn.de/record/53793 Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. eng info:eu-repo/semantics/openAccess