Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

By Krithika Manohar, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton. IEEE Control Systems, 2017

471 citations recorded by the source corpus.

Optimal sensor and actuator placement is an important unsolved problem in control theory. Nearly every downstream control decision is affected by these sensor and actuator locations, but determining optimal locations amounts to an intractable brute-force search among the combinatorial possibilities. Indeed, there are (np) = n!/((n-p)!p!) possible choices of p point sensors out of an n-dimensional state x. Determining optimal sensor and actuator placement in general, even for linear feedback control, is an open challenge. Instead, sensor and actuator locations are routinely chosen according to heuristics and intuition. For moderate-sized search spaces, the sensor placement problem has well-known model-based solutions using optimal experiment design [1], [2], and information theoretic and Bayesian criteria [3]-[7]. As discussed in "Summary," this article explores how to design optimal sensor locations for signal reconstruction in a framework that scales to arbitrarily large problems, leveraging modern techniques in machine learning and sparse sampling. Reducing the number of sensors through principled selection may be critically enabling when sensors are costly, and it may also enable faster state estimation for low-latency, high-bandwidth control.

Subfields: Artificial Intelligence, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1fff309330f85146134e49e0022ac61ac60506a9

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Citations in the corpus

Machine Learning for Fluid Mechanics (2019, background): “Machine learning and sparse algorithms are synergistic, in that underlying low-dimensional representations facilitate sparse measurements (Manohar et al. 2018) and fast randomized computations (Halko et al. 2011).”
Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data (2020, background): “As an alternative, sparsity-promoting representation techniques, e.g., compressed sensing, have also been demonstrated to be able to achieve the same goal more robustly when data is noisy [14, 15].”