MODELING ENVIRONMENT NOISE FOR TRAINING NEURAL NETWORKS

An approach for altering alter training data and training process associated with a neural network to emulate environmental noise and operational instrument error by using the concepts of shots to sample within a squeezed space model, wherein shots are an uncertainty index that is the average of all shots from a sampling, is disclosed. The approach leverages a squeeze theorem to create a squeezed space model based on the regression of the upper and lower bound associated with the environmental noise and instrument error. The approach calculates an average noise index based on the squeezed space model, wherein the index is used to alter the training data and process.