  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.
