Abstract: A plurality of perturbed seed data values may be generated by performing a plurality of perturbation operations on an initial value to be processed by a predictive model. A plurality of counterfactual operations may be performed to generate a plurality of background data values of a background data store based on respective ones of the plurality of perturbed seed data values, a reference value within a domain of a predictive model, and the predictive model. A model analysis engine may be executed to generate a model analysis of the predictive model utilizing the background data store and the initial value.

Abstract: Systems and methods are provided for simulating quantile behavior of a physical system. A plurality of parameter samples to a physical system are accessed and a subset of the parameter samples are identified, each of the plurality of parameter samples including a variation of parameters for the physical system. The physical system is simulated based on the subset of the parameter samples to generate simulation results, each of the subset of the parameter samples corresponding to a respective one of the simulation results. A neural network is trained to predict the simulation results based on the subset of the parameter samples.