Multiscale simulations are a well-accepted way to bridge the length and time scales required
for scientific studies with the solution accuracy achievable through available computational
resources. Traditional approaches either solve a coarse model with selective refinement or
coerce a detailed model into faster sampling, both of which have limitations. Here, we
present a paradigm of adaptive, multiscale simulations that couple different scales using a
dynamic-importance sampling approach. Our method uses machine learning to dynamically …