We present an adaptive sampling met

We present an adaptive sampling method supplemented by a distributed database and a prediction
method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme
integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum
and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD)
simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces
a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud
database Redis provides the plain table lookup, and with locality aware hashing we gather input data
for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its
uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring
points. We find that our adaptive scheme significantly improves simulation performance by a factor of
2.5–25, while retaining high accuracy for various choices of the algorithm parameters.