A0311
Title: Adaptive grid designs for classifying monotonic binary simulations
Authors: Tian Bai - Beijing Insititute of Technology (China) [presenting]
Xu He - Chinese Academy of Sciences (China)
Dianpeng Wang - Beijing Institute of Technology (China)
Abstract: The motivation is the need for effective classification in ice-breaking dynamic simulations aimed at determining the conditions under which an underwater vehicle will break through the ice. This simulation is extremely time-consuming and yields deterministic, binary, and monotonic outcomes. Detecting the critical edge between the negative-outcome and positive-outcome regions with minimal simulation runs necessitates an efficient experimental design for selecting input values. Adaptive designs, which sequentially select input values based on obtained outcomes, outperform static designs significantly by eliminating redundant points without losing information. A new class of adaptive designs is proposed called adaptive grid designs. An adaptive grid is a sequence of grids with increasing resolution, such that lower-resolution grids are proper subsets of higher-resolution grids. By prioritizing simulation runs at lower resolution points and skipping redundant runs, adaptive grid designs require an order of magnitude fewer simulation runs to ensure a certain level of classification accuracy than the best possible static design and the same order of magnitude of runs as the best possible adaptive design. Numerical results across test functions, the road crash simulation, and the ice-breaking simulation validate the superiority of adaptive grid designs.
