  In this paper, we present a machine learning-based data generator framework
tailored to aid researchers who utilize simulations to examine various physical
systems or processes. High computational costs and the resulting limited data
often pose significant challenges to gaining insights into these systems or
processes. Our approach involves a two-step process: initially, we train a
supervised predictive model using a limited simulated dataset to predict
simulation outcomes. Subsequently, a reinforcement learning agent is trained to
generate accurate, simulation-like data by leveraging the supervised model.
With this framework, researchers can generate more accurate data and know the
outcomes without running high computational simulations, which enables them to
explore the parameter space more efficiently and gain deeper insights into
physical systems or processes. We demonstrate the effectiveness of the proposed
framework by applying it to two case studies, one focusing on earthquake
rupture physics and the other on new material development.
