CLAIMED: A CLAssification-Incorporated Minimum Energy Design to explore a multivariate response surface with feasibility constraints
This addresses the challenge of efficiently exploring high-dimensional input-output spaces with feasibility constraints for physics simulations, representing an incremental improvement in optimization methods.
The paper tackles the problem of exploring a deterministic multivariate response surface to find input combinations that produce outputs close to a target vector, particularly in optimizing force-field systems in physics, and proposes an approach combining machine learning with experimental design to locate multiple good regions in the input space.
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.