标题： 基于SIREN的压力场重建：一种用于复杂噪声环境中的无网格图像测速方法 显示英文标题

标题： Pressure Field Reconstruction with SIREN: A Mesh-Free Approach for Image Velocimetry in Complex Noisy Environments

摘要： 本工作提出了一种新颖的方法，利用SIREN（正弦表示网络）从图像测速数据中重建压力场，强调其在噪声环境中的有效性以及作为隐式神经表示的无网格特性。 虽然我们简要评估了两种最近提出的 方法——一次矩阵全向积分（OS-MODI）和格林函数积分（GFI），但主要重点是SIREN方法的优势。 OS-MODI技术在无噪声条件和结构化网格中表现良好，但在应用于高纵横比的非结构化网格时遇到困难。 同样，GFI方法由于牛顿核固有的奇异性而遇到困难。 相比之下，所提出的SIREN方法是一种无网格方法，可以直接重建压力场，无需内在的网格连接，从而避免与病态单元和非结构化网格相关的问题。 这为传统基于网格的方法提供了明显的优势。 此外，研究表明，SIREN架构的变化可用于过滤测速数据中的固有噪声。 本工作将SIREN定位为一种强大且通用的压力重建解决方案，特别是在缺乏网格结构的噪声环境中，为该领域开创了新的创新应用途径。 显示英文摘要

摘要： This work presents a novel approach for pressure field reconstruction from image velocimetry data using SIREN (Sinusoidal Representation Network), emphasizing its effectiveness as an implicit neural representation in noisy environments and its mesh-free nature. While we briefly assess two recently proposed methods - one-shot matrix-omnidirectional integration (OS-MODI) and Green's function integral (GFI) - the primary focus is on the advantages of the SIREN approach. The OS-MODI technique performs well in noise-free conditions and with structured meshes but struggles when applied to unstructured meshes with high aspect ratio. Similarly, the GFI method encounters difficulties due to singularities inherent from the Newtonian kernel. In contrast, the proposed SIREN approach is a mesh-free method that directly reconstructs the pressure field, bypassing the need for an intrinsic grid connectivity and, hence, avoiding the challenges associated with ill-conditioned cells and unstructured meshes. This provides a distinct advantage over traditional mesh-based methods. Moreover, it is shown that changes in the architecture of the SIREN can be used to filter out inherent noise from velocimetry data. This work positions SIREN as a robust and versatile solution for pressure reconstruction, particularly in noisy environments characterized by the absence of mesh structure, opening new avenues for innovative applications in this field.