Photoelasticity enables full-field stress analysis in transparent objects
through stress-induced birefringence. Existing techniques are limited to 2D
slices and require destructively slicing the object. Recovering the internal 3D
stress distribution of the entire object is challenging as it involves solving
a tensor tomography problem and handling phase wrapping ambiguities. We
introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress
tensor fields as neural implicit representations from polarization
measurements. Our key insight is to jointly handle phase unwrapping and tensor
tomography using a differentiable forward model based on Jones calculus. Our
non-linear model faithfully matches real captures, unlike prior linear
approximations. We develop an experimental multi-axis polariscope setup to
capture 3D photoelasticity and experimentally demonstrate that NeST
reconstructs the internal stress distribution for objects with varying shape
and force conditions. Additionally, we showcase novel applications in stress
analysis, such as visualizing photoelastic fringes by virtually slicing the
object and viewing photoelastic fringes from unseen viewpoints. NeST paves the
way for scalable non-destructive 3D photoelastic analysis.

通过应力诱导双折射，光弹性能使透明物体的压力分析变得全场，但现有技术只限于二维切片且需要对物体进行破坏性切割。我们引入了 NeST，这是一种通过极化测量从神经隐式表示重建三维应力张量场的分析合成方法。我们的关键见解是使用基于琼斯计算的可微分正向模型共同处理相位展开和张量层析成像。我们开发了一个实验性的多轴偏振仪设置来捕捉三维光弹性，并在实验中展示了 NeST 对具有不同形状和受力条件的物体内部应力分布的重建。此外，我们展示了应力分析中的新颖应用，例如通过虚拟切割物体来可视化光弹性条纹并从未见过的视角观察光弹性条纹。NeST 为可扩展的非破坏性三维光弹性分析铺平了道路。

NeST: 利用三维光弹性进行神经应力张量层析成像

NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity

Deep neural networks (DNNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal
DNN architecture for large applications is challenging. Common approaches go
for deeper and larger DNN architectures but may incur substantial redundancy.
To address these problems, we introduce a network growth algorithm that
complements network pruning to learn both weights and compact DNN architectures
during training. We propose a DNN synthesis tool (NeST) that combines both
methods to automate the generation of compact and accurate DNNs. NeST starts
with a randomly initialized sparse network called the seed architecture. It
iteratively tunes the architecture with gradient-based growth and
magnitude-based pruning of neurons and connections. Our experimental results
show that NeST yields accurate, yet very compact DNNs, with a wide range of
seed architecture selection. For the LeNet-300-100 (LeNet-5) architecture, we
reduce network parameters by 70.2x (74.3x) and floating-point operations
(FLOPs) by 79.4x (43.7x). For the AlexNet and VGG-16 architectures, we reduce
network parameters (FLOPs) by 15.7x (4.6x) and 30.2x (8.6x), respectively.
NeST's grow-and-prune paradigm delivers significant additional parameter and
FLOPs reduction relative to pruning-only methods.

论文介绍了一种名为 NeST 的 DNN 综合工具，它采用增长和修剪的方式来自动学习权重和紧凑的 DNN 网络架构，以生成准确且很小的 DNN 网络。