The paper applies the Extreme Learning Machines (ELMs) to inverse reactor problems.
Multi-group transport model is used for the inversion as opposed to point kinetics.
ELMs are compared against Artificial Neural Networks (ANNs).
Various options are tested to improve the reliability of the estimation.
Results highlight the potential of the ELM approach.