软音声晶体的拓设计可调节带间隙:深度学习方法
Jingru Li1, Minqi Qian1, Jingming Yin1
1School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.
Materials (Basel, Switzerland)
|January 25, 2025
概括
一个新的深度学习框架使软音声晶体的智能反向设计成为可能. 这种方法有效地产生了可调节带间隙与预应力,为先进的材料设计提供了灵活性和准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 声学 声学 在声学方面
- 计算物理 计算物理
背景情况:
- 软的音声晶体在较大的变形下表现出可调节的带间隙.
- 当前的反向设计方法 (梯度驱动/免费) 通常是缓慢的,缺乏智能,需要灵敏度分析.
研究的目的:
- 开发一个智能,数据驱动的框架,用于软音声晶体的反向设计.
- 在预压下建立所需带间隙和材料拓之间的映射.
主要方法:
- 一个深度学习框架,结合了条件变量自编码器和多层感知器.
- 材料构成性质的非线性超弹性新胡克模型.
- 用布洛赫理论进行带结构计算的转移矩阵方法.
主要成果:
- 该框架高效地生成了多个软音声晶体设计,并预测了预压.
- 实现的频段间隙与目标规格密切匹配,证明了准确性和灵活性.
- 探索设计空间以优化停止带以获得更宽的带宽和更低的频率.
结论:
- 提出的深度学习方法为软音声晶体的传统反向设计方法提供了一个智能和高效的替代方案.
- 该框架成功生成了具有增强波衰减性能的结构.
- 这种数据驱动的方法有助于发现具有量身定制的声学特性的新型软材料.
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