论文标题
声称：分类的最低能量设计，以探索具有可行性约束的多元响应表面
CLAIMED: A CLAssification-Incorporated Minimum Energy Design to explore a multivariate response surface with feasibility constraints
论文作者
论文摘要
由使用大规模计算机模拟在物理中优化力场系统的问题的动机，我们考虑探索确定性复杂的多元响应表面。目的是找到输入组合，以生成接近某些或“目标”向量的输出。尽管将问题降低为一维损耗函数的输入空间，但由于输入和输入空间的高维度以及在输入空间中的多个“理想”区域的高维度，搜索是非凡的，并且具有挑战性，并且在输入空间中多个“理想的”区域以及对目标函数的难度与抗毒剂模型的难度。我们提出了一种基于将机器学习技术与智能实验设计思想相结合的方法，以在输入空间中找到多个良好的区域。
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.
