A novel corrective-source term approach to modeling unknown physics in aluminum extraction process. - Haakon Robinson; Erlend Lundby; Adil Rasheed; Jan Tommy Gravdahl (CoRR, 2022)

Sparse Neural Networks with Skip-Connections for Identification of Aluminum Electrolysis Cell. - Erlend Torje Berg Lundby; Haakon Robinson; Adil Rasheed; Ivar Johan Halvorsen; Jan Tommy Gravdahl (CDC, 2023)

A Comparative Study of Sparsity Promoting Techniques in Neural Network for Modeling Non-Linear Dynamics. - Emil Johannesen Haugstvedt; Alberto Miño Calero; Erlend Torje Berg Lundby; Adil Rasheed; Jan Tommy Gravdahl (IEEE Access, 2023)

Sparse neural networks with skip-connections for nonlinear system identification. - Erlend Torje Berg Lundby; Haakon Robinson; Adil Rasheed; Ivar Johan Halvorsen; Jan Tommy Gravdahl (CoRR, 2023)

Deep active learning for nonlinear system identification. - Erlend Torje Berg Lundby; Adil Rasheed; Ivar Johan Halvorsen; Dirk Reinhardt; Sebastien Gros; Jan Tommy Gravdahl (CoRR, 2023)

Deep learning assisted physics-based modeling of aluminum extraction process. - Haakon Robinson; Erlend Lundby; Adil Rasheed; Jan Tommy Gravdahl (Eng. Appl. Artif. Intell., 2023)

Data-Driven Dynamical Modeling in the Face of Data Limitations. - Erlend Lundby (2023)

Sparse deep neural networks for modeling aluminum electrolysis dynamics. - Erlend Torje Berg Lundby; Adil Rasheed; Jan Tommy Gravdahl; Ivar Johan Halvorsen (Appl. Soft Comput., 2023)