  This paper addresses the problem of identifying different flow environments
from sparse data collected by wing strain sensors. Insects regularly perform
this feat using a sparse ensemble of noisy strain sensors on their wing. First,
we obtain strain data from numerical simulation of a Manduca sexta hawkmoth
wing undergoing different flow environments. Our data-driven method learns
low-dimensional strain features originating from different aerodynamic
environments using proper orthogonal decomposition (POD) modes in the frequency
domain, and leverages sparse approximation to classify a set of strain
frequency signatures using a dictionary of POD modes. This bio-inspired machine
learning architecture for dictionary learning and sparse classification permits
fewer costly physical strain sensors while being simultaneously robust to
sensor noise. A measurement selection algorithm identifies frequencies that
best discriminate the different aerodynamic environments in low-rank POD
feature space. In this manner, sparse and noisy wing strain data can be
exploited to robustly identify different aerodynamic environments encountered
in flight, providing insight into the stereotyped placement of neurons that act
as strain sensors on a Manduca sexta hawkmoth wing.
