神经折射率场：释放背景导向纹影层析成像在体积流可视化中的潜力
Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization
AI总结：
针对背景导向纹影层析成像（BOST）基于体素网格的缺陷，提出神经折射率场（NeRIF）方法，通过神经网络隐式表示流场，提升重建精度、分辨率并降低成本，且可适配多种层析模式。
摘要：
背景导向纹影层析成像（BOST）是一种用于可视化复杂湍流的常用方法，因其易于实施且能捕捉多种流动参数的三维分布而受到重视。然而，基于体素的网格方案带来了重大挑战，如空间分辨率不足、离散化误差大、抗噪性差和计算成本过高。本工作提出一种创新的重建方法，称为神经折射率场（NeRIF），该方法通过神经网络隐式表示流场，神经网络采用定制策略进行训练。对湍流本生灯火焰的数值模拟和实验演示均表明，我们的方法可显著提高重建精度和空间分辨率，同时降低计算成本。尽管本文在背景导向纹影层析成像的背景下展示，但NeRIF中嵌入的核心思想可轻松适用于其他多种层析成像模式，包括层析吸收光谱和层析粒子成像测速，从而扩大其在流可视化和分析不同领域的潜在影响。
英文摘要：
Background-oriented Schlieren tomography (BOST) is a prevalent method for visualizing intricate turbulent flows, valued for its ease of implementation and capacity to capture three-dimensional distributions of a multitude of flow parameters. However, the voxel-based meshing scheme leads to significant challenges, such as inadequate spatial resolution, substantial discretization errors, poor noise immunity, and excessive computational costs. This work presents an innovative reconstruction approach termed neural refractive index field (NeRIF) which implicitly represents the flow field with a neural network, which is trained with tailored strategies. Both numerical simulations and experimental demonstrations on turbulent Bunsen flames suggest that our approach can significantly improve the reconstruction accuracy and spatial resolution while concurrently reducing computational expenses. Although showcased in the context of background-oriented schlieren tomography here, the key idea embedded in the NeRIF can be readily adapted to various other tomographic modalities including tomographic absorption spectroscopy and tomographic particle imaging velocimetry, broadening its potential impact across different domains of flow visualization and analysis.
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。
