Efficient, real-time and automated data analysis is one of the key elements for achieving scientific success in complex engineering and physical systems, two examples of which include the JET and ITER tokamaks.One problem which is common to these fields is the determination of the pulsation modes from an irregularly sampled time series. To this end, there are a wealth of signal processing techniques that are being applied to post-pulse and real-time data analysis in such complex systems. Here, we wish to present a review of the applications of a method based on the sparse representation of signals, using examples of the synergies that can be exploited when combining ideas and methods from very different fields, such as astronomy, astrophysics and thermonuclear fusion plasmas.Examples of this work in astronomy and astrophysics are the analysis of pulsation modes in various classes of stars and the orbit determination software of the Pioneer spacecraft. Two examples of this work in thermonuclear fusion plasmas include the detection of magneto-hydrodynamic instabilities, which is now performed routinely in JET in real-time on a sub-millisecond time scale, and the studies leading to the optimization of the magnetic diagnostic system in ITER and TCV.These questions have been solved by formulating them as inverse problems, despite the fact that these applicative frameworks are extremely different from the classical use of sparse representations, from both the theoretical and computational point of view. The requirements, prospects and ideas for the signal processing and real-time data analysis applications of this method to the routine operation of ITER will also be discussed.Finally, a very recent development has been the attempt to apply this method to the deconvolution of the measurement of electric potential performed during a ground-based survey of a proto-Villanovian necropolis in central Italy.
Interpretation of machine-learning-based disruption models for plasma control