Challenges and opportunities for machine learning in multiscale computational modeling
It provides a perspective on opportunities and challenges for researchers in mechanical engineering and computational modeling, but it is incremental as it reviews existing developments rather than presenting new results.
This paper addresses the computational challenges in multiscale modeling by exploring how machine learning can serve as a surrogate or accelerator for traditional numerical methods, achieving comparable accuracy with significantly faster speeds to facilitate solving complex systems.
Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data and discretization dependence, interpretability, and data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.