  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.
