Felsch Gerrit, Slesarenko Viacheslav
Cluster of Excellence livMatS @ FIT - Freiburg Center for Interactive Materials and Bioinspired Technologies, 79110, Freiburg, Germany.
Department of Microsystems Engineering, University of Freiburg, 79110, Freiburg, Germany.
Sci Rep. 2024 Aug 20;14(1):19397. doi: 10.1038/s41598-024-70364-z.
Generative machine learning models have shown notable success in identifying architectures for metamaterials-materials whose behavior is determined primarily by their internal organization-that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that this perceived success in the employment of generative models for metamaterials might be akin to survivorship bias. We assess the performance of the four most popular generative models-the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), the Wasserstein GAN (WGAN), and the Denoising Diffusion Probabilistic Model (DDPM)-in generating kirigami structures. Prohibiting cut intersections can prevent the identification of an appropriate similarity measure for kirigami metamaterials, significantly impacting the effectiveness of VAE and WGAN, which rely on the Euclidean distance-a metric shown to be unsuitable for considered geometries. This imposes significant limitations on employing modern generative models for the creation of diverse metamaterials.
生成式机器学习模型在识别超材料（其行为主要由内部结构决定）的结构以匹配特定目标特性方面已取得显著成功。通过研究剪纸超材料（其中切口之间的相关性产生复杂的设计限制），我们证明，在超材料中使用生成模型时这种看似成功的情况可能类似于幸存者偏差。我们评估了四种最流行的生成模型——变分自编码器（VAE）、生成对抗网络（GAN）、瓦瑟斯坦生成对抗网络（WGAN）和去噪扩散概率模型（DDPM）——在生成剪纸结构方面的性能。禁止切口相交会妨碍为剪纸超材料确定合适的相似性度量，从而显著影响依赖欧几里得距离（一种已证明不适用于所考虑几何形状的度量）的VAE和WGAN的有效性。这对使用现代生成模型来创建各种超材料施加了重大限制。
