We contrast the distinct frameworks of materials design and physical learning
in creating elastic networks with desired stable states. In design, the desired
states are specified in advance and material parameters can be optimized on a
computer with this knowledge. In learning, the material physically experiences
the desired stable states in sequence, changing the material so as to stabilize
each additional state. We show that while designed states are stable in
networks of linear Hookean springs, sequential learning requires specific
non-linear elasticity. We find that such non-linearity stabilizes states in
which strain is zero in some springs and large in others, thus playing the role
of Bayesian priors used in sparse statistical regression. Our model shows how
specific material properties allow continuous learning of new functions through
deployment of the material itself