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首页> 外文期刊>Physical Review X >Supervised Learning in Physical Networks: From Machine Learning to Learning Machines
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Supervised Learning in Physical Networks: From Machine Learning to Learning Machines

机译：在物理网络中监督学习：从机器学习到学习机器

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摘要

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 .
机译：材料和机器通常以特定的目标设计，因此它们表现出对给定力或约束的理想反应。在这里，我们探索替代方法，即物理耦合学习。在此范例中，系统最初不会设计用于完成任务，而是物理上适应应用力以发展执行任务的能力。至关重要的是，我们需要通过物理合理的学习规则促进耦合学习，这意味着学习只需要本地响应，并且没有关于所需功能的明确信息。我们表明，这种本地学习规则可以用于任何物理网络，无论是平衡还是处于稳态，具有特定于两个特定系统，即无序的流量网络和弹性网络。通过申请和调整统计学习理论对物理世界的进步，我们展示了新类智能超材料的合理性，能够适应用户需求？原位。

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