## 1 Introduction and Motivation  
High-order harmonic generation (HHG) is a well-established experimental technique for obtaining coherent radiation in the XUV to soft X-ray spectral domain on the attosecond timescale [1; 2]. The process of HHG is highly nonlinear and has very low efficiency [3]. Despite the fact that high-harmonic radiation is the result of the highly nonlinear interaction between the atom and the driving laser pulse, this method is reliable and provides coherent secondary sources in the UV-XUV-soft-X-ray spectral domain with a table-top setup. The ELI-ALPS pan-European research infrastructure is also based on HHG sources [4], demonstrating that the increasing research efforts dedicated to this field are fully justified. Given the advantages of exploring HHG in the emerging field of Attosecond Science and Technology, considerable research effort is being dedicated to find ways for increasing high-harmonic efficiency by implementing either tight [5] or loose focusing configurations [4; 6], inventing different quasi-phase-matching techniques [7; 8; 9; 10; 11; 12; 13; 14], multi-color and/or multi-jet configurations [15; 12; 16; 17], but also to extend the available photon energy to reach the water-window [18; 19; 17] region. Numerical approaches are unavoidable in this research for several reasons: (i) often, new experiments are designed based on predictions resulting from simulations; (ii) interpretation of existing experimental results cannot be done without additional results from models; (iii) when looking for the best possible outcome within given experimental possibilities, reliable simulation tools can considerably contribute in finding the best solution. Increasing efficiency and/or finding the optimum photon flux obtainable within a restricted parameter space is crucial. Experimental scan along many tunable parameters is very time and resource consuming, additionally the experimental uncertainties result in variations from one measurement to the next. Simulation with a complete 3D model seems a more convenient route. However, as we will show in the following section, scanning along multiple parameters, typically 4-5, implies the execution and interpretation of hundreds to thousands of simulation runs. Since the amount of raw data and computational effort is very difficult to be managed, we propose an additional method to extract the needed information from the data pool, and to predict possible outcomes for parameter combinations which were not included in the original simulation sets. The need for specialized algorithms capable to perform optimization procedures has been expressed, and evolutionary algorithms have been constructed to achieve this goal, see for example an early review on the adaptive control techniques [20]. Recently, genetic algorithms were used to optimize the optical waveforms [21; 22; 23] coming from a light-wave synthesizer with the goal either to extend the high-harmonic cutoff or to enhance the high-harmonic efficiency. In this respect, artificial neural networks (ANN) are known to be high speed mathematical models that are capable of solving linear and non-linear multivariate regression problems [24; 25], being generally used for their excellent prediction capabilities [26; 27] but also for their capacity to solve problems such as classification [28; 29], pattern recognition [30] or system control [31; 32; 33]. Inspired by the human brain, these numerical modelling techniques are able to gain high efficiency and accuracy in the presence of uncertainties due to their ability to learn from experience [34]. Based on these properties and also on their effectiveness in processing large amount of data, the ANNs have applicability in various fields such as medicine [35], engineering [36; 37], science technology [38], nanotechnology [39; 40] or physics [41].