@inproceedings{53793,
  abstract     = {{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.}},
  author       = {{Harder, Hans and Peitz, Sebastian}},
  booktitle    = {{Systems theory in data and optimization}},
  editor       = {{Berberich, J. and Ianelli, A. and Allgöwer, F.}},
  isbn         = {{978-3-031-83191-1}},
  keywords     = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}},
  pages        = {{153--168}},
  publisher    = {{Springer}},
  title        = {{{Solving partial differential equations with equivariant extreme learning machines}}},
  doi          = {{10.1007/978-3-031-83191-1_10}},
  year         = {{2025}},
}