Hybrid Models That Combine Machine Learning and Simulations
Philippe J. Giabbanelli · Computing in Science & Engineering · 2022
Simulation experts are now well acquainted with machine learning (ML) techniques, using them to find patterns in data that can later be turned into rules of a simulation or enabling their simulated entities to adapt and learn. In the other direction, ML experts occasionally make use of simulated data to create controlled experiments in which learning algorithms can be evaluated. In this article, we go beyond these typical uses by focusing on current opportunities that have the potential to bring the two research communities together. These opportunities can be realized in areas where the potential of hybrid ML/simulation methods has not been fully attained yet. Such applications also motivate the development of innovative methods, for example, to combine the accuracy of ML with the interpretability of simulation models. Using select examples from our interdisciplinary team, this article reflects on opportunities in applications and techniques to promote productive conversations across research areas.