Sparsity enabled cluster reduced-order models
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Sparsity-enabled CROM computes cluster-based reduced-order models (CROM) from compressed data and allows one to find few optimized sensor locations tailored to the specific model. Estimating a CROM from those compressed or few point measurements preserves the model structure and topology as compared to model estimated from the full data. The publication is available on arXiv.


  1. Clone this repository to your desktop.
  2. Add path to sparseCROM/src folder to Matlab search path using addpath('<path to mds>/sparseCROM/src').


For determining the optimized sensor locations tailored to a specific CROM, the following packages need to be installed.

  1. Sparse Sensor Placement Optimization (SSPOC), which sets up the optimization problem. It is sufficient to add the file SSPOC.m to the source folder sparseCROM/src.

  2. The optimization problem is solved using the cvx toolbox, which needs to be installed.

Getting Started

See examples/example.m for demonstrating the approach on the period double gyre flow, a simplified model of the gulf stream ocean front. Just execute this file in MatLab and it will generate the plot files in examples/output.

License (CiteMe OSS)

See the LICENSE file for details.