preprint unpublished Hans Harder author 98879 Sebastian Peitz author 474270000-0002-3389-793X We utilize extreme learning machines for the prediction of partial differential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 eng extreme learning machinespartial differential equationsdata-driven predictionhigh-dimensional systems Harder, Hans, and Sebastian Peitz. <i>Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines</i>. @article{Harder_Peitz, title={Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}, author={Harder, Hans and Peitz, Sebastian} } Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.” . Harder, H., &#38; Peitz, S. (n.d.). <i>Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines</i>. Harder, Hans, and Sebastian Peitz. “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines,” n.d. H. Harder, S. Peitz, (n.d.). 537932024-04-30T08:43:14Z2024-04-30T08:45:24Z