学术文献库

Currently, machine learning (ML) methods are widely used to process the results of physical experiments. In some cases, due to the limited amount of experimental data, ML-models can be pre-trained on synthetic data simulated based on the analytical theory and then fine-tuned using experimental data. A limitation of this approach is the presence of the latent parameters of the analytical model, which values are difficult or impossible to estimate. Setting these parameters incorrectly may induce a dataset shift even when applied to synthetic data. To overcome this problem, we train the ML-model on a dataset with randomly varied latent parameters of the analythical model to force the ML-model to concentrate on more general patterns that depend weakly on the latent parameters. We applied this approach to the problem of tight focusing of a laser pulse with the complex structure of the wavefront. We observed good accuracy of reconstructing of the tilt parameters when training and testing the ML-model on datasets generated for different values of the latent parameters. This confirms that the ML-model was able to select relevant information without over-fitting for specific features inherent in certain values of the latent parameters. We believe that this approach will enrich possible applications of ML-methods to an experimental diagnostics of laser pulses.