Neural Network Surrogate Modeling of a Synthetic Bi-Physical Second Order Low-Pass Filter

In this work, an initial step forward to address the development of multi-physics neural modeling is presented. The initial step consists of a prove of concept to determine if a neural network model (NN) is enough accurate to represent the bi-physical response (electrical and thermal) of a second order low-pass filter. Three different learning algorithms were used to train the neural network, being the Bayesian Regularization the model which exhibits the best generalization performance to represent the EM responses subject to changes in the circuit temperature.