Active learning–augmented end-to-end modeling toward fast inverse design in chirped pulse amplification

To capture the nonlinear dynamics and gain evolution in chirped pulse amplification (CPA) systems, the split-step Fourier method and the fourth-order Runge-Kutta method are integrated to iteratively address the generalized nonlinear Schrdinger equation and the rate equations.However, this approach is burdened by substantial computational demands, resulting in significant time expenditures.In the context of intelligent laser optimization and inverse design, the necessity for numerous simulations further exacerbates this issue, highlighting the need for fast and accurate simulation methodologies.Here, we introduce an end-to-end model augmented with active learning (E2E-AL) with decent generalization through different dedicated embedding methods over various parameters.On an identical computational platform, the artificial intelligence-driven model is 2000 times faster than the conventional simulation method.Benefiting from the active learning strategy, the E2E-AL model achieves decent precision with only two-thirds of the training samples compared with the case without such a strategy.Furthermore, we demonstrate a multi-objective inverse design of the CPA systems enabled by the E2E-AL model.The E2E-AL framework manifests the potential of becoming a standard approach for the rapid and accurate modeling of ultrafast lasers and is readily extended to simulate other complex systems.