  Throughout computational science, there is a growing need to utilize the
continual improvements in raw computational horsepower to achieve greater
physical fidelity through scale-bridging over brute-force increases in the
number of mesh elements. For instance, quantitative predictions of transport in
nanoporous media, critical to hydrocarbon extraction from tight shale
formations, are impossible without accounting for molecular-level interactions.
Similarly, inertial confinement fusion simulations rely on numerical diffusion
to simulate molecular effects such as non-local transport and mixing without
truly accounting for molecular interactions. With these two disparate
applications in mind, we develop a novel capability which uses an active
learning approach to optimize the use of local fine-scale simulations for
informing coarse-scale hydrodynamics. Our approach addresses three challenges:
forecasting continuum coarse-scale trajectory to speculatively execute new
fine-scale molecular dynamics calculations, dynamically updating coarse-scale
from fine-scale calculations, and quantifying uncertainty in neural network
models.
