Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.

中文翻译：

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机械神经网络：学习行为的架构材料

除了一些活组织之外，很少有材料能够在长时间暴露于意料之外的环境负载情况下自主学习表现出所需的行为。在不断变化的条件下（例如，内部损坏程度的上升、不同的夹具场景和波动的外部负载），仍然有更少的材料可以继续表现出先前习得的行为，同时还能获得最适合当前情况的新行为。在这里，我们描述了一类称为机械神经网络 (MNN) 的架构材料，它通过调整其组成梁的刚度来实现这种学习能力，类似于人工神经网络 (ANN) 调整其权重的方式。制造了一个示例晶格以证明其同时学习多种机械行为的能力，并进行了一项研究，以确定晶格尺寸、包装配置、算法类型、行为数和线性与非线性刚度可调性对 MNN 学习的影响。因此，这项工作为可以学习行为和属性的人工智能 (AI) 材料奠定了基础。