利用低维特征和基于注意力的神经网络从含噪射线照片中重建 Richtmyer-Meshkov 不稳定性
Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
AI总结：
本文提出基于注意力的 transformer 网络，从受模糊、散射和噪声污染的射线照片特征序列中重建类 ICF 双壳模拟中的 Richtmyer-Meshkov 不稳定性拓扑并准确恢复增长率。
摘要：
经过训练的基于注意力的 transformer 网络能够从一系列流体力学特征中稳健地恢复 Richtmyer-Meshkov 不稳定性产生的复杂拓扑，这些特征提取自受到模糊、散射和噪声破坏的射线照相图像。该方法在类 ICF 双壳流体力学模拟上得到了验证。该网络的关键组成部分是一个 transformer encoder，作用于从含噪射线照片中提取的特征序列。该 encoder 包含多个自注意力层，用于学习输入序列中的时间依赖关系并增强模型的表达能力。结果表明，即使气体-金属界面被射线照相噪声严重遮蔽，该方法仍具有出色能力，可准确恢复 Richtmyer-Meshkov 不稳定性增长率。
英文摘要：
A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell hydrodynamic simulations. The key component of this network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is demonstrated to exhibit an excellent ability to accurately recover the Richtmyer-Meshkov instability growth rates, even despite the gas-metal interface being greatly obscured by radiographic noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
