  This work discusses a novel method for estimating the location of a gas
source based on spatially distributed concentration measurements taken, e.g.,
by a mobile robot or flying platform that follows a predefined trajectory to
collect samples. The proposed approach uses a Physics-Guided Neural Network to
approximate the gas dispersion with the source location as an additional
network input. After an initial offline training phase, the neural network can
be used to efficiently solve the inverse problem of localizing the gas source
based on measurements. The proposed approach allows avoiding rather costly
numerical simulations of gas physics needed for solving inverse problems. Our
experiments show that the method localizes the source well, even when dealing
with measurements affected by noise.
