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.
翻译:基于利用大规模计算机模拟来优化物理学中的力量场系统的问题,我们考虑探索一个确定性复杂多变反应表面,目的是找到产生接近某些理想或“目标”矢量的产出的投入组合。尽管在一维损失功能方面减少了对输入空间探索的问题,但由于输入组合不可行、输入和输出空间的高度维度以及输入空间中多个“理想”区域,以及难以用替代模型来很好地模拟目标功能,因此搜索是非技术性和具有挑战性的。我们提出了一个方法,其基础是将机器学习技术与智能实验设计构想结合起来,以便在输入空间中找到多个良好的区域。