High-resolution multiphysics predictions and multifields reconstruction for chemical lasers enabled by operator neural networks

Numerical simulation is an important approach for chemical laser investigation. However, it is time-consuming and prone to numerical divergence because of the system's complexity and strong nonlinearity. This paper develops a deep learning encoding underlying physics method for modeling chemical oxygen-iodine lasers (COIL). Two classes of DeepONets are proposed to capture the nonlinear relationships between flow and optical fields . The DeepONet’s capability of generalization outside the function space of the dataset is obtained, despite being trained by a noisy dataset. The high-frequency oscillations in the laser data increase the DeepONet prediction errors on optical quantities. Two solutions, subdomain division with sampling refinement and filtering high-frequency component of the laser by Fourier transform , are proposed to address this issue and achieve a nearly 50 % error reduction. The pre-trained DeepONet modules, functioning as building blocks, are utilized to construct parallel and serial DeepM&Mnets through which only 10 discrete data points among 22,877 are used to reconstruct all physical fields for COIL with errors below 10 %. The serial DeepM&Mnet method leverages limited laser measurement data to reconstruct flow field and component distributions, marking a significant step forward in solving the inverse problem in chemical laser systems.