Sparse deep neural networks for modeling aluminum electrolysis dynamics

Erlend Torje Berg Lundby, Adil Rasheed, Jan Tommy Gravdahl, Ivar Johan Halvorsen

Abstract: Highlights•Using sparse neural networks to model set of nonlinear ODEs representing an aluminum electrolysis cell.•Increased interpretability with sparse neural networks.•Higher generalizability for sparse neural networks than dense neural networks given small datasets.•Sparse neural networks show more stable convergence in training than their dense counterpart.