• Corpus ID: 226281776

Sparsely Constrained Neural Networks for Model Discovery of PDEs

@article{Both2021SparselyCN,
  title={Sparsely Constrained Neural Networks for Model Discovery of PDEs},
  author={Gert-Jan Both and Remy Kusters},
  journal={ArXiv},
  year={2021},
  volume={abs/2011.04336}
}
Sparse regression on a library of candidate features has developed as the prime method to discover the PDE underlying a spatio-temporal dataset. As these features consist of higher order derivatives, model discovery is typically limited to low-noise and dense datasets due to the erros inherent to numerical differentiation. Neural network-based approaches circumvent this limit, but to date have ignored advances in sparse regression algorithms. In this paper we present a modular framework that… 

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