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.

翻译：本文提出了一种基于机器学习的数据生成器框架，旨在辅助利用模拟研究各种物理系统或过程的研究人员。高昂的计算成本及由此产生的有限数据，常常对深入理解这些系统或过程构成重大挑战。我们的方法包含两个步骤：首先，使用有限的模拟数据集训练一个监督预测模型，以预测模拟结果；随后，训练一个强化学习智能体，通过利用该监督模型来生成准确、类似模拟的数据。借助此框架，研究人员无需运行高计算成本的模拟即可生成更准确的数据并获知结果，使他们能够更高效地探索参数空间，并更深入地洞察物理系统或过程。我们通过两个案例研究展示了该框架的有效性，其中一个聚焦于地震破裂物理学，另一个则关注新材料开发。