cs.SD - 2023-10-19

Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties

• paper_url: http://arxiv.org/abs/2310.12869
• repo_url: None
• paper_authors: Han Zhang, Rayehe Karimi Mahabadi, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson
• for: 本研究使用不确定性评估技术，即光谱投影和多项式混沌扩展，减少声学材料特性和几何缺陷的抽象响应频谱的采样需求。
• methods: 本研究使用了一种可读性高、分辨率独立的方法对几何缺陷进行编码，并将其与 Монте卡洛、规则评估和稀Grid采样相结合，以生成高精度的输出空间概率分布。
• results: 研究发现，通过 combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate model fitting, 在1D和7D输入空间场景中可以实现单位采样减少至10^0和10^1，同时保持高精度的输出空间概率分布。
  Abstract

  This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given stochastic material properties and geometric defects. A novel method of encoding geometric defects in an interpretable, resolution independent is showcased in the formation of input space probability distributions. Orders of magnitude sampling reductions down to $\sim10^0$ and $\sim10^1$ are achieved in the 1D and 7D input space scenarios respectively while maintaining accurate output space probability distributions through combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate model fitting.
  摘要