论文标题
机械超材料中隐性组合规则的机器学习
Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials
论文作者
论文摘要
拼图，折纸和（元）材料设计中出现的组合问题具有罕见的解决方案，该解决方案定义了配置空间中的复杂且鲜明的界限。这些边界很难用常规的统计和数值方法捕获。在这里，我们表明卷积神经网络可以学会识别组合机械超材料的这些界限，尽管使用了大量的采样训练集，但仍可以成功地概括。这表明该网络从稀疏训练集中渗透了基本的组合规则，从而为（元）材料的复杂设计开辟了新的可能性。
Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.
