Abstract:
Background The reaction kinetics in lasers often involves a lot of excited state species. The mutual effects and numerical stiffness arising from the excited state species pose significant challenges in numerical simulations of lasers. The development of artificial intelligence has made neural networks (NNs) a promising approach to address the computational intensity and instability in excited state reaction kinetics (ESRK).
Purpose However, the complexity of ESRK poses challenges for NN training. These reactions involve numerous species and mutual effects, resulting in a high-dimensional variable space. This demands that the NN possess the capability to establish complex mapping relationships. Moreover, the significant change in state before and after the reaction leads to a broad variable space coverage, which amplifies the demand for NN’s accuracy.
Methods To address the aforementioned challenges, this study introduced successful sequence-to-sequence learning from large language learning into ESRK to enhance prediction accuracy in complex, high-dimensional regression. Additionally, a statistical regularization method was proposed to improve the diversity of the outputs. NNs with different architectures were trained using randomly sampled data, and their capabilities were compared and analyzed.
Results The proposed method is validated using a vibrational reaction mechanism for hydrogen fluoride, which involves 16 species and 137 reactions. The results demonstrate that the sequential model achieves lower training loss and relative error during training. Furthermore, experiments with different hyperparameters reveal that variation in the random seed can significantly impact model performance.
Conclusions In this work, the introduction of the sequential model successfully reduced the parameter count of the conventional wide model without compromising accuracy. However, due to the intrinsic complexity of ESRK, there remains considerable room for improvement in NN-based regression tasks for this domain.
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