Probabilistic invertible neural network for inverse design space exploration and reasoning

Abstract

Invertible neural network (INN) is a promising tool for inverse design optimization. While generating forward predictions from given inputs to the system response, INN enables the inverse process without much extra cost. The inverse process of INN predicts the possible input parameters for the specified system response qualitatively. For the purpose of design space exploration and reasoning for critical engineering systems, accurate predictions from the inverse process are required. Moreover, INN predictions lack effective uncertainty quantification for regression tasks, which increases the challenges of decision making. This paper proposes the probabilistic invertible neural network (P-INN): the epistemic uncertainty and aleatoric uncertainty are integrated with INN. A new loss function is formulated to guide the training process with enhancement in the inverse process accuracy. Numerical evaluations have shown that the proposed P-INN has noticeable improvement on the inverse process accuracy and the prediction uncertainty is reliable.