mlsanityData to 7 Oct 2026

Data-Driven Sparse Sensor Placement for Reconstruction

arXiv 1701.07569 ↗First posted 26 Jan 2017math.OC cs.SY eess.SYcited by 57

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Abstract (arXiv)

Optimal sensor placement is a central challenge in the design, prediction, estimation, and control of high-dimensional systems. High-dimensional states can often leverage a latent low-dimensional representation, and this inherent compressibility enables sparse sensing. This article explores optimized sensor placement for signal reconstruction based on a tailored library of features extracted from training data. Sparse point sensors are discovered using the singular value decomposition and QR pivoting, which are two ubiquitous matrix computations that underpin modern linear dimensionality reduction. Sparse sensing in a tailored basis is contrasted with compressed sensing, a universal signal recovery method in which an unknown signal is reconstructed via a sparse representation in a universal basis. Although compressed sensing can recover a wider class of signals, we demonstrate the benefits of exploiting known patterns in data with optimized sensing. In particular, drastic reductions in the required number of sensors and improved reconstruction are observed in examples ranging from facial images to fluid vorticity fields. Principled sensor placement may be critically enabling when sensors are costly and provides faster state estimation for low-latency, high-bandwidth control. MATLAB code is provided for all examples.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jan 2017.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 9; 2024: 8; 2025: 10; 2026: 18.

57 papers.

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  1. 2021Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningcited by 55
  2. 2022Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal datacited by 37
  3. 2023Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flowscited by 30
  4. 2023The transformative potential of machine learning for experiments in fluid mechanicscited by 26
  5. 2023A DeepONet multi-fidelity approach for residual learning in reduced order modelingcited by 14
  6. 2023Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstructioncited by 12
  7. 2025PhySense: Sensor Placement Optimization for Accurate Physics Sensingcited by 11
  8. 2025Guiding diffusion models to reconstruct flow fields from sparse datacited by 11
  9. 2021PySensors: A Python Package for Sparse Sensor Placementcited by 8
  10. 2021A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systemscited by 8
  11. 2025Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environmentscited by 8
  12. 2023Constrained optimization of sensor placement for nuclear digital twinscited by 6
38 more
  1. 2021Active operator inference for learning low-dimensional dynamical-system models from noisy datacited by 5
  2. 2021Differentiable Feature Selection, a Reparameterization Approachcited by 4
  3. 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
  4. 2021Inadequacy of Linear Methods for Minimal Sensor Placement and Feature Selection in Nonlinear Systems; a New Approach Using Secantscited by 3
  5. 2023Data-Induced Interactions of Sparse Sensors Using Statistical Physicscited by 3
  6. 2026Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systemscited by 3
  7. 2021Model discovery in the sparse sampling regimecited by 2
  8. 2022Super-resolution GANs of randomly-seeded fieldscited by 2
  9. 2024GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equationscited by 2
  10. 2024Predictive Digital Twin for Condition Monitoring Using Thermal Imagingcited by 2
  11. 2025Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoringcited by 2
  12. 2023Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalizationcited by 1
  13. 2024Computationally and Memory-Efficient Robust Predictive Analytics Using Big Datacited by 1
  14. 2024Revisiting Score Function Estimators for k-Subset Samplingcited by 1
  15. 2024Physics-aligned Schr"odinger bridgecited by 1
  16. 2024State Estimation Using Sparse DEIM and Recurrent Neural Networkscited by 1
  17. 2024Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networkscited by 1
  18. 2025Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolationcited by 1
  19. 2026Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methodscited by 1
  20. 2026UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engressioncited by 1
  21. 2026FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitivescited by 1
  22. 2021Characterization of Frequent Online Shoppers using Statistical Learning with Sparsitycited by 0
  23. 2022Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fieldscited by 0
  24. 2023Information-Based Sensor Placement for Data-Driven Estimation of Unsteady Flowscited by 0
  25. 2023Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensingcited by 0
  26. 2024A physics-driven sensor placement optimization methodology for temperature field reconstructioncited by 0
  27. 2025Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Trainingcited by 0
  28. 2025Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)cited by 0
  29. 2025Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: An Application and Evaluation of Data-Driven Sparse Sensingcited by 0
  30. 2025Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascadecited by 0
  31. 2025Adaptive sampling using variational autoencoder and reinforcement learningcited by 0
  32. 2026Generalized Inverses of Matrix Products: From Fundamental Subspaces to Randomized Decompositionscited by 0
  33. 2026Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilationcited by 0
  34. 2026Adaptive Sensing of Continuous Physical Systems for Machine Learningcited by 0
  35. 2026LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)cited by 0
  36. 2026Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learningcited by 0
  37. 2026Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributionscited by 0
  38. 2026Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approachcited by 0

50 most cited of 57 All 57 (JSON)