Data analysis techniques

This page collects information on data analysis techniques used in fusion research.

Temporal analysis

Linear analysis

Correlation
Fourier analysis
Wavelet analysis
Conditional analysis
Probability distributions

Non-linear analysis

Bicoherence, bispectrum
Chaos analysis (Strange attractor, Fractal dimension, Mutual information, Lyapunov exponent)
Hilbert-Huang transform

Self-similarity

Rescaled range or Hurst analysis; Structure functions
Waiting times, quiet times ^[1]

Spatial analysis

Most of the techniques listed under 'temporal analysis' can of course be applied to spatial data.

Tomography (cf. TJ-II:Tomography)

Spatio-temporal analysis

Biorthogonal decomposition

Image analysis

Twodimensional Fourier analysis
Twodimensional wavelet analysis
Event detection using thresholding
Optical flow (for movies)

Integrated data analysis

The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement.

References

↑ [ http://link.aps.org/doi/10.1103/PhysRevE.66.036124 R. Sánchez et a., Quiet-time statistics: A tool to probe the dynamics of self-organized-criticality systems from within the strong overlapping regime, Phys. Rev. E 66, 036124 (2002)]

[1] [ http://link.aps.org/doi/10.1103/PhysRevE.66.036124 R. Sánchez et a., Quiet-time statistics: A tool to probe the dynamics of self-organized-criticality systems from within the strong overlapping regime, Phys. Rev. E 66, 036124 (2002)]

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