Learning is traditionally studied in biological or computational systems. The power of learning frameworks in solving hard inverse problems provides an appealing case for the development of physical learning in which physical systems adopt desirable properties on their own without computational design. It was recently realized that large classes of physical systems can physically learn through local learning rules, autonomously adapting their parameters in response to observed examples of use. We review recent work in the emerging field of physical learning, describing theoretical and experimental advances in areas ranging from molecular self-assembly to flow networks and mechanical materials. Physical learning machines provide multiple practical advantages over computer designed ones, in particular by not requiring an accurate model of the system, and their ability to autonomously adapt to changing needs over time. As theoretical constructs, physical learning machines afford a novel perspective on how physical constraints modify abstract learning theory.

中文翻译：

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在物理系统中没有神经元的学习


传统上，学习是在生物或计算系统中研究的。学习框架在解决困难的逆问题方面的力量为物理学习的发展提供了一个有吸引力的案例，其中物理系统在没有计算设计的情况下自行采用理想的属性。最近人们意识到，大类物理系统可以通过本地学习规则进行物理学习，根据观察到的使用示例自主调整其参数。我们回顾了新兴物理学习领域的最新工作，描述了从分子自组装到流网和机械材料等领域的理论和实验进展。与计算机设计的学习机相比，物理学习机具有多种实际优势，特别是不需要精确的系统模型，并且它们能够随着时间的推移自主适应不断变化的需求。作为理论结构，物理学习机为物理约束如何修改抽象学习理论提供了新的视角。