mlsanityData to 7 Oct 2026

PySensors: A Python Package for Sparse Sensor Placement

arXiv 2102.13476 ↗First posted 20 Feb 2021eess.SP cs.LG math.OCcited by 8

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Abstract (arXiv)

PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-driven sparse sensor placement optimization for reconstruction (SSPOR) and sparse sensor placement optimization for classification (SSPOC). In this work we provide a brief description of the mathematical algorithms and theory for sparse sensor optimization, along with an overview and demonstration of the features implemented in PySensors (with code examples). We also include practical advice for user and a list of potential extensions to PySensors. Software is available at https://github.com/dynamicslab/pysensors.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 46 references that are in the map. First posted on arXiv 20 Feb 2021.

Builds on

8 of 46 references.

References · 8

  1. 2017Data-Driven Sparse Sensor Placement for Reconstructioncited by 57
  2. 2016Adaptive Least Mean Squares Estimation of Graph Signalscited by 13
  3. 2014On Submodularity and Controllability in Complex Dynamical Networkscited by 10
  4. 2020Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraintscited by 10
  5. 2018Greedy Sensor Placement with Cost Constraintscited by 5
  6. 2017Predicting shim gaps in aircraft assembly with machine learning and sparse sensingcited by 3
  7. 2016Subsampling for Graph Power Spectrum Estimationcited by 2
  8. 2018Neural-inspired sensors enable sparse, efficient classification of spatiotemporal datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 2.

8 papers.

  1. 2023A DeepONet multi-fidelity approach for residual learning in reduced order modelingcited by 14
  2. 2025NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learningcited by 11
  3. 2025PhySense: Sensor Placement Optimization for Accurate Physics Sensingcited by 11
  4. 2023Constrained optimization of sensor placement for nuclear digital twinscited by 6
  5. 2023Data-Induced Interactions of Sparse Sensors Using Statistical Physicscited by 3
  6. 2024A physics-driven sensor placement optimization methodology for temperature field reconstructioncited by 0
  7. 2026Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applicationscited by 0
  8. 2026Origins and mitigation of double descent in reduced order modelingcited by 0