# PySensors: A Python Package for Sparse Sensor Placement

arXiv: https://arxiv.org/abs/2102.13476 · HTML: https://mlsanity.com/paper/2102.13476/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Data-Driven Sparse Sensor Placement for Reconstruction](https://mlsanity.com/paper/1701.07569/) (2017)
- [Adaptive Least Mean Squares Estimation of Graph Signals](https://mlsanity.com/paper/1602.05703/) (2016)
- [On Submodularity and Controllability in Complex Dynamical Networks](https://mlsanity.com/paper/1404.7665/) (2014)
- [Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints](https://mlsanity.com/paper/2005.03650/) (2020)
- [Greedy Sensor Placement with Cost Constraints](https://mlsanity.com/paper/1805.03717/) (2018)
- [Predicting shim gaps in aircraft assembly with machine learning and sparse sensing](https://mlsanity.com/paper/1711.08861/) (2017)
- [Subsampling for Graph Power Spectrum Estimation](https://mlsanity.com/paper/1603.03697/) (2016)
- [Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data](https://mlsanity.com/paper/1804.07884/) (2018)

## Built on by (most cited first, 8 of 8)

- [A DeepONet multi-fidelity approach for residual learning in reduced order modeling](https://mlsanity.com/paper/2302.12682/) (2023)
- [NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning](https://mlsanity.com/paper/2503.16323/) (2025)
- [PhySense: Sensor Placement Optimization for Accurate Physics Sensing](https://mlsanity.com/paper/2505.18190/) (2025)
- [Constrained optimization of sensor placement for nuclear digital twins](https://mlsanity.com/paper/2306.13637/) (2023)
- [Data-Induced Interactions of Sparse Sensors Using Statistical Physics](https://mlsanity.com/paper/2307.11838/) (2023)
- [A physics-driven sensor placement optimization methodology for temperature field reconstruction](https://mlsanity.com/paper/2409.18423/) (2024)
- [Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications](https://mlsanity.com/paper/2606.24989/) (2026)
- [Origins and mitigation of double descent in reduced order modeling](https://mlsanity.com/paper/2607.26414/) (2026)

## 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.