Just as it happens in many other research areas, concrete examples of using machine learning demonstrate the potential of using it for opening entirely new pathways in both theoretical and experimental studies of high-intensity laser-matter interactions (see Gonoskov et al. SciRep 9, 7043 (2019)). These include methods for reconstructing the interaction scenarios with limited diagnostics as well as for retrieving indicative features, symmetries, self-similarities and other peculiarities. This can not only guide theoretical developments but also establish a new paradigm for understanding and mastering complex systems hardly accessible for traditional analytical methods. Apart from the general interest, further studies in this area are strongly motivated by the necessity of developing a systematic basis for retrieving information in modern experiments on the high-intensity laser facilities in Sweden (the Relativistic Attosecond Physics Laboratory at Umeå University and the Lund Laser Centre) and worldwide (ELI-NP, ELI-beamlines, the Central Laser Facility at RAL, the COREALS laser in South Korea and others). Despite the ever increasing sophistication of the numerical tools, several well-known difficulties have re-emerged with greater severity along the path to using large-scale simulations for experimental design and analysis at these facilities: the initial conditions for the interactions are not well characterized and vary from experiment to experiment, while the diagnostics produce a limited output that is difficult to disentangle.
In this project we intend to use the computational resources of SNIC for further development and practical use of our novel infrastructure that combines simulations and data analysis based on machine learning in the area of laser-matter interactions. Our primary goal is to trigger and support a new kind of experiments that are based on retrieving information from solving the inverse problem with artificial neural networks that are trained with massive sets of direct simulations performed with previously developed and tested numerical tools.