Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

使用Transformer架构和基于图的超参数优化技术提出了一种改善多模态输出场景中预测准确性的新方法，在稀疏实验数据与模拟数据相结合时，该方法不仅能有效减少模拟偏差，而且与以前的方法相比，预测准确性更高。我们在惯性约束聚变实验和这些实验的合成版本上展示了我们方法的效果，在这些实验中，只有10次真实世界数据可用。

基于Transformer的代理模型弥合了极少数据下的ICF模拟与实验差距