Codes

Computational tools and repositories from the group and our collaborators.

Matlab
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Spectral Proper Orthogonal Decomposition (SPOD)

Matlab implementation of frequency-domain POD for extracting optimal dynamic structures from stationary random processes. Each mode oscillates at a single frequency and optimally accounts for statistical variability.
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LinStab2D: Stability and resolvent analysis of compressible viscous flows

User-friendly MATLAB tool for analyzing 2D compressible and incompressible flows. Supports temporal/global stability, spatial/temporal-spatial stability, and resolvent analyses using sparse finite-difference discretizations.
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Bispectral Mode Decomposition (BMD)

Extracts coherent flow structures associated with triadic interactions from experimental or numerical data. Identifies resonant three-wave interactions and distinguishes sum- and difference-interactions through mode bispectrum and interaction maps.
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SLICK: Predictive flow ROM

A stochastic data-driven reduced-order model applicable to a wide range of turbulent natural and engineering flows is presented. Combining ideas from Koopman theory and spectral model order reduction, the stochastic low-dimensional inflated convolutional Koopman model (SLICK) accurately forecasts short-time transient dynamics while preserving long-term statistical properties.
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Spectral EOF (SEOF)

Spectral empirical orthogonal function decomposition of reanalysis data, equivalent to SPOD but using atmospheric science terminology where POD is called EOF. Educes weather and climate patterns like the Madden-Julian Oscillation, Quasi-Biennial Oscillation, and El Niño-Southern Oscillation from ERA-Interim and ERA-20C datasets.
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Streaming SPOD

Streaming algorithm for computing SPOD of stationary processes with incremental updates from single snapshots. Low memory requirement enables real-time deployment and convergence from arbitrarily long data streams.

Python
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PySPOD: Spectral Proper Orthogonal Decomposition

Parallel Python implementation of SPOD for analyzing large quasi-stationary datasets. Distributes spatial dimensions while preserving time to avoid FFT bottlenecks. Includes strong and weak scalability for big data modal analysis.