A machine learning approach for constrained sensor placement

Sensor placement is of pivotal importance in closed-loop control as measurements are key to design the control laws. In this article, a novel machine learning-based sensor placement algorithm is proposed in order to recover a high-dimensional field from a limited amount of local measurements with a linear estimator. Unlike many other methods, our algorithm does not rely on a reduced order model and achieves good results even with a small number of sensors. In many situations, sensors cannot be placed arbitrarily, either because of their geometry or because of the environment they are in. Our algorithm naturally accounts for these constraints as well as being robust to noise. Its performance is illustrated on a fluid flow example and compared to two state of the art methods, Effective Independence and FrameSense, on the recovery of the pressure field from limited noisy pressure measurements.