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The validation of a theory is commonly based on appealing to clearlydistinguishable and describable features in properly reduced experimental data,while the use of ab-initio simulation for interpreting experimental datatypically requires complete knowledge about initial conditions and parameters.We here apply the methodology of using machine learning for overcoming thesenatural limitations. We outline some basic universal ideas and show how we canuse them to resolve long-standing theoretical and experimental difficulties inthe problem of high-intensity laser-plasma interactions. In particular we showhow an artificial neural network can "read" features imprinted in laser-plasmaharmonic spectra that are currently analysed with spectral interferometry.

Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics