Abstract: The present invention is related to a learning-based surrogate model capable of predicting operating condition-dependent time and/or frequency domain waveforms at the IC pins. The model of the present invention consists of a set of discrete features that can be extracted from a time-domain waveform and can be used to reconstruct the continuous time-domain waveform accurately and consequently reconstruct the frequency-domain characteristics of the time-domain waveform. The model of the present invention has a machine-learning framework e.g. an Artificial Neural Network, which can be trained to predict all the features extracted as a function of parameters characterizing the operating conditions.