Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
arXiv:2606.24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal, and physical-variable dimensions. Reconstructing these fields from sparse sensor measurements is a fundamental challenge in environmental monitoring, digital twins, forecasting, and data assimilation. Existing low-cost reconstruction approaches are commonly based on matrix decompositions, which require multidimensional datasets to be flattened into two-dimensional snapshot matrices, thereby disca
The increasing complexity of urban environments and the demand for real-time monitoring necessitate advanced techniques for handling high-dimensional sensor data efficiently.
This development offers a more efficient and accurate method for environmental monitoring and predictive modeling, critical for smart cities and resource management.
The ability to reconstruct complex data from sparse measurements more accurately, using high-order tensor methods, improves the quality of environmental decision-making and digital twins.
- · Environmental monitoring agencies
- · Smart city developers
- · Urban planning departments
- · AI/ML research in environmental sciences
- · Legacy environmental modeling techniques
- · Organizations relying on less sophisticated data reconstruction
- · Cities without advanced sensor infrastructure
Improved accuracy in predicting urban flow and air quality, leading to better public health outcomes and resource allocation.
Accelerated development of more sophisticated digital twins for urban environments, integrating diverse sensor data streams.
Potential for new regulatory frameworks and policy instruments based on higher-fidelity, real-time urban environmental intelligence.
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