Ghabriel A. Gomes de Sá

Physics-informed neural networks without a phenomenological model available – applications in regression problems

This work shows that, even in training approaches involving a weight initialization strategy coupled with a constructive algorithm for defining the number of hidden units, the neural model does not ensure that static gain signals between the output and inputs are consistent with the physical reality of the phenomenon.