论文标题
物理网络中的监督学习：从机器学习到学习机器
Supervised learning in physical networks: From machine learning to learning machines
论文作者
论文摘要
材料和机器通常考虑到特定的目标，因此它们对给定的力或约束表现出理想的反应。在这里，我们探索了一种替代方法，即身体耦合学习。在此范式中，该系统最初不是为完成任务而设计的，而是物理适应应用的力来开发执行任务的能力。至关重要的是，我们需要通过身体上合理的学习规则来促进学习的学习，这意味着学习只需要本地响应，而无需有关所需功能的明确信息。我们表明，无论是平衡还是稳定状态，都可以为任何物理网络得出这样的本地学习规则，并特别关注两个特定系统，即流动网络和弹性网络。通过将统计学习理论的进步应用和调整到物理世界中，我们证明了能够适应用户需求的新型智能超材料的合理性。
Materials and machines are often designed with particular goals in mind, so that they exhibit desired responses to given forces or constraints. Here we explore an alternative approach, namely physical coupled learning. In this paradigm, the system is not initially designed to accomplish a task, but physically adapts to applied forces to develop the ability to perform the task. Crucially, we require coupled learning to be facilitated by physically plausible learning rules, meaning that learning requires only local responses and no explicit information about the desired functionality. We show that such local learning rules can be derived for any physical network, whether in equilibrium or in steady state, with specific focus on two particular systems, namely disordered flow networks and elastic networks. By applying and adapting advances of statistical learning theory to the physical world, we demonstrate the plausibility of new classes of smart metamaterials capable of adapting to users' needs in-situ.
