  Mechanical metamaterials are usually designed to show desired responses to
prescribed forces. In some applications, the desired force-response
relationship might be hard to specify exactly, although examples of forces and
corresponding desired responses are easily available. Here we propose a
framework for supervised learning in a thin creased sheet that learns the
desired force-response behavior from training examples of spatial force
patterns and can then respond correctly to previously unseen test forces.
During training, we fold the sheet using different training forces and assume a
learning rule that changes stiffness of creases in response to their folding
strain. We find that this learning process reshapes non-linearities inherent in
folding a sheet so as to show the correct response for previously unseen test
forces. We study the relationship between training error, test error and sheet
size which plays the role of model complexity. Our framework shows how the
complex energy landscape of disordered mechanical materials can be reshaped
using an iterative local learning rule.
