# Discovering PDEs from Multiple Experiments

@article{Tod2021DiscoveringPF, title={Discovering PDEs from Multiple Experiments}, author={Georges Tod and Gert-Jan Both and Remy Kusters}, journal={ArXiv}, year={2021}, volume={abs/2109.11939} }

Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experiments have inherent natural variability in parameters, initial and boundary conditions that cannot be simply averaged out. We introduce a randomised adaptive group Lasso sparsity estimator to promote grouped sparsity and implement it in a deep learning based PDE discovery framework1. It allows to create a learning…

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