生成模型在剪纸超材料上表现不佳
Generative models struggle with kirigami metamaterials
AI总结：
研究评估VAE、GAN、WGAN和DDPM生成剪纸超材料的能力，发现切口约束及欧氏距离失配导致模型效果受限，揭示既有成功可能存在幸存者偏差。
摘要：
生成式机器学习模型在识别与特定目标属性相匹配的超材料架构方面已取得显著成功；超材料是指其行为主要由内部组织结构决定的材料。通过研究剪纸超材料——其中切口之间的依赖关系产生复杂的设计约束——我们证明，这种在超材料中应用生成模型所感知到的成功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在生成剪纸结构时的性能：变分自编码器（VAE）、生成对抗网络（GAN）、Wasserstein GAN（WGAN）和去噪扩散概率模型（DDPM）。禁止切口相交会阻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氏距离的VAE和WGAN的有效性；该度量被证明不适合所考虑的几何结构。这给使用现代生成模型创建多样化超材料带来了重大限制。
英文摘要：
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.
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