  Motivated by the problem of optimization of force-field systems in physics
using large-scale computer simulations, we consider exploration of a
deterministic complex multivariate response surface. The objective is to find
input combinations that generate output close to some desired or "target"
vector. In spite of reducing the problem to exploration of the input space with
respect to a one-dimensional loss function, the search is nontrivial and
challenging due to infeasible input combinations, high dimensionalities of the
input and output space and multiple "desirable" regions in the input space and
the difficulty of emulating the objective function well with a surrogate model.
We propose an approach that is based on combining machine learning techniques
with smart experimental design ideas to locate multiple good regions in the
input space.
