论文标题
在光弹性材料中迫使重建的机器学习方法
Machine learning approach to force reconstruction in photoelastic materials
论文作者
论文摘要
光弹性技术在颗粒材料中应力的定性和定量分析中具有悠久的传统。在过去的二十年中，许多不同的实验团队已经开发了从光弹性响应中重建力之间重建力的计算方法。不幸的是，所有这些方法在计算上都是昂贵的。这限制了它们用于处理广泛的数据集的用途，这些数据集捕获了由大量粒子组成的颗粒集合的时间演变。在本文中，我们提出了一种新的问题，该方法利用了卷积神经网络的力量来识别复杂的空间模式。使用神经网络的主要缺点是训练它们通常需要一个大型标记的数据集，这很难在实验上获得。我们表明，可以通过在大型合成数据集上预处网络，然后在较小的实验数据集上进行微调来成功规避这个问题。由于我们目前缺乏实验数据，我们通过更改所考虑的粒子的大小来证明方法的潜力，从而改变了所展示的光弹性模式，而不是典型的实验误差。
Photoelastic techniques have a long tradition in both qualitative and quantitative analysis of the stresses in granular materials. Over the last two decades, computational methods for reconstructing forces between particles from their photoelastic response have been developed by many different experimental teams. Unfortunately, all of these methods are computationally expensive. This limits their use for processing extensive data sets that capture the time evolution of granular ensembles consisting of a large number of particles. In this paper, we present a novel approach to this problem which leverages the power of convolutional neural networks to recognize complex spatial patterns. The main drawback of using neural networks is that training them usually requires a large labeled data set which is hard to obtain experimentally. We show that this problem can be successfully circumvented by pretraining the networks on a large synthetic data set and then fine-tuning them on much smaller experimental data sets. Due to our current lack of experimental data, we demonstrate the potential of our method by changing the size of the considered particles which alters the exhibited photoelastic patterns more than typical experimental errors.
