Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given set of properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific sizes. One should, therefore, find multiple microarchitectural designs that exhibit the desired properties for a specimen with given dimensions. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture, meaning that peak stresses should be minimized as well. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, we propose a modular approach titled 'Deep-DRAM' that combines four decoupled models, including two deep learning models (DLM), a deep generative model (DGM) based on conditional variational autoencoders (CVAE), and direct finite element (FE) simulations. Deep-DRAM (deep learning for the design of random-network metamaterials) integrates these models into a unified framework capable of finding many solutions to the multi-objective inverse design problem posed here. The integrated framework first introduces the desired elastic properties to the DGM, which returns a set of candidate designs. The candidate designs, together with the target specimen dimensions are then passed to the DLM which predicts their actual elastic properties considering the specimen size. After a filtering step based on the closeness of the actual properties to the desired ones, the last step uses direct FE simulations to identify the designs with the minimum peak stresses.

翻译：机械元材料的实际应用往往涉及解决反向问题, 目标是找到( 多式) 微结构, 从而产生一定的属性。 添加制造技术的有限分辨率往往要求解决特定大小的反向问题。 因此, 应当找到多种显微结构设计, 显示给定尺寸标本的预期属性。 此外, 候选微结构应该耐疲劳和骨折, 意思是峰值压力也应该被最小化。 这种多目标的微结构设计问题是难以解决的, 但它的解决方案是实际应用机械元材料的关键。 在这里, 我们提出一个模块化方法, 名为“ 深- DRAM ”, 它将四个脱形模型结合起来, 包括两个深度学习模型( DLM ), 一个基于有条件的变异性自动测仪( CVAE ) 的深微结构模型, 以及直接限值元素( FE) 模拟。 深- DRAM ( 继续学习随机网络设计, 但它是机械化的精细缩缩缩缩图案的精度) 将这些模型与理想的缩略图解模型整合框架整合成一个框架, 向后, 将许多的缩缩图返回。