  The lateral-line system that has evolved in many aquatic animals enables them
to navigate murky fluid environments, locate and discriminate obstacles. Here,
we present a data-driven model that uses artificial neural networks to process
flow data originating from a stationary sensor array located away from an
obstacle placed in a potential flow. The ability of neural networks to estimate
complex underlying relationships between parameters, in the absence of any
explicit mathematical description, is first assessed with two basic potential
flow problems: single source/sink identification and doublet detection.
Subsequently, we address the inverse problem of identifying an obstacle shape
from distant measures of the pressure or velocity field. Using the analytical
solution to the forward problem, very large training data sets are generated,
allowing us to obtain the synaptic weights by means of a gradient-descent based
optimization. The resulting neural network exhibits remarkable effectiveness in
predicting unknown obstacle shapes, especially at relatively large distances
for which classical linear regression models are completely ineffectual. These
results have far-reaching implications for the design and development of
artificial passive hydrodynamic sensing technology.
