AI 中文总结
该研究提出基于模拟的成像（SBI）框架，用全模拟数据训练的二维卷积神经网络作为声学逆问题实时求解器，实现低成本便携成像，在噪声和传感器减少时仍保持良好重建性能。
AI 中文摘要
我们提出基于模拟的成像（Simulation-Based Imaging，SBI），这是一种用于非破坏性声学成像的框架，其中完全在模拟数据上训练的机器学习模型作为声学逆问题的实时求解器。高保真节点间断伽辽金正求解器通过在单位立方体域内随机包含物几何结构生成大型训练数据集；随后，二维卷积神经网络学习从边界压力测量值到内部32×32×32体素重建的直接映射。训练后的模型可在无需包含物数量或几何结构先验知识的情况下，从144个边界传感器可靠恢复包含物的位置和大小。在5%的加性测量噪声下，重建误差仅降低13%；仅使用传感器阵列的17%（144个传感器中的24个）即可达到与全覆盖相差4%以内的质量。这些结果证明SBI是一种可行的概念验证成像设备，其复杂性存在于软件而非硬件中，为廉价、便携、可部署的成像系统开辟了道路。
英文摘要
We introduce Simulation-Based Imaging (SBI), a framework for non-destructive acoustic imaging in which machine learning models trained entirely on simulated data serve as real-time solvers for the acoustic inverse problem. A high-fidelity nodal Discontinuous Galerkin forward solver generates large training datasets by randomizing inclusion geometry within a unit-cube domain; a 2D convolutional neural network then learns a direct mapping from boundary pressure measurements to a 32 by 32 by 32 voxel reconstruction of the interior. The trained model reliably recovers inclusion position and size from 144 boundary sensors with no prior knowledge of inclusion count or geometry. Reconstruction error degrades by only 13% under 5% additive measurement noise, and just 17% of the sensor array (24 of 144 sensors) suffices for quality within 4% of full coverage. These results establish SBI as a viable proof-of-concept imaging device whose complexity resides in software rather than hardware, opening a path toward cheap, portable, deployable imaging systems.
Comments15 pages, 4 figures
