Researchers from Skoltech and the Shanghai Institute of Optics and Fine Mechanics have developed a novel method to optimize attosecond light pulses, a technology essential for ultrafast physics experiments. Attosecond pulses are incredibly short flashes of light used to study electron dynamics, magnetic materials, and chiral molecules. However, tuning the parameters of the laser-plasma sources that generate these pulses has traditionally been slow and computationally expensive. The new approach uses a neural network to dramatically accelerate this tuning process, as detailed in a recent study published in Communications in Nonlinear Science and Numerical Simulation. Selecting the correct settings for these light sources requires complex modeling. The response of the plasma mirror depends on numerous variables, and running full particle-in-cell simulations to verify each potential configuration consumes substantial computing time. To overcome this bottleneck, the team combined physical modeling with machine learning. They trained a neural network, specifically a multilayer perceptron with Fourier encoding, on the results of one-dimensional particle-in-cell simulations. Once trained, this model can rapidly predict the ellipticity of the reflected attosecond pulse, which is a key polarization parameter, based on the specific problem conditions. In practice, this hybrid strategy works by using the neural network to quickly scan and evaluate new configurations. It acts as a surrogate model that narrows down promising settings, leaving only a limited number of precise checks to the full, time-consuming physical simulation. This method is significantly more efficient than traditional brute-force parameter sweeps. According to the researchers, the model identifies parameter sets that yield higher ellipticity and demonstrates stable performance across varying laser characteristics and target parameters. Sergey Rykovanov, head of the AI and Supercomputing Laboratory at the Skoltech AI Center, highlighted the primary benefit of this work. He noted that the main challenge in such problems is the high cost of direct physical simulation due to the large parameter space and the computational resources required for every validation run. By combining a neural-network surrogate model with accurate calculations, the team has shown it is possible to significantly accelerate the search for optimal regimes without sacrificing physical relevance. The proposed approach not only makes the practical design of attosecond pulse sources with tailored polarization more accessible at a lower computational cost but also demonstrates scalability. The method can be extended to higher-dimensional parameter spaces, offering a template for solving other problems where expensive physical simulations need to be accelerated. This innovation represents a significant step forward in making advanced light sources more efficient and widely usable for scientific research.