Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures
Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures
复制标题
DOI：
10.1115/1.4052300
复制
发表时间：
2021-09
影响因子：
3.3
通讯作者：
Manaswin Oddiraju;A. Behjat;M. Nouh;Souma Chowdhury
中科院分区：
文献类型：
--
作者：
Manaswin Oddiraju;A. Behjat;M. Nouh;Souma Chowdhury
自动化逆向设计方法是开发具有特殊用户需求特性的超材料系统的关键。虽然机器学习方法代表了超材料结构设计中的新兴范例，但仍然缺乏按需检索逆向设计的能力。这种能力在加速基于优化的逆向设计过程中是有用的。本文开发了一个逆向设计框架，通过可逆神经网络（INNs）的新用途提供这种能力。我们利用了一个INN架构，可以训练它在一组高保真样本上执行前向预测，并自动学习具有保证可逆性的反向映射。我们应用此INN建模的周期性和非周期性的声子结构的频率响应，与表现在振动抑制的钻杆。训练和测试样本是通过采用传递矩阵方法生成的。与典型的深度神经网络（DNN）相比，INN模型提供了具有竞争力的正向和反向预测性能。这些INN模型用于检索查询的非谐振频率范围的近似逆设计;逆设计然后用于初始化基于约束梯度的优化过程，以找到更准确的逆设计，同时最大限度地减少质量。INN初始化的优化被发现在查询属性和质量方面通常上级随机初始化和逆DNN初始化的优化。粒子群优化与INN派生的初始点，然后发现提供更好的解决方案，特别是对于高维非周期性结构。
Automated inverse design methods are critical to the development of metamaterial systems that exhibit special user-demanded properties. While machine learning approaches represent an emerging paradigm in the design of metamaterial structures, the ability to retrieve inverse designs on-demand remains lacking. Such an ability can be useful in accelerating optimization-based inverse design processes. This paper develops an inverse design framework that provides this capability through the novel usage of invertible neural networks (INNs). We exploit an INN architecture that can be trained to perform forward prediction over a set of high-fidelity samples and automatically learns the reverse mapping with guaranteed invertibility. We apply this INN for modeling the frequency response of periodic and aperiodic phononic structures, with the performance demonstrated on vibration suppression of drill pipes. Training and testing samples are generated by employing a transfer matrix method. The INN models provide competitive forward and inverse prediction performance compared to typical deep neural networks (DNNs). These INN models are used to retrieve approximate inverse designs for a queried non-resonant frequency range; the inverse designs are then used to initialize a constrained gradient-based optimization process to find a more accurate inverse design that also minimizes mass. The INN-initialized optimizations are found to be generally superior in terms of the queried property and mass compared to randomly initialized and inverse DNN-initialized optimizations. Particle swarm optimization with INN-derived initial points is then found to provide even better solutions, especially for the higher-dimensional aperiodic structures.