论文标题
通过深层神经网络学习隐藏的弹性
Learning hidden elasticity with deep neural networks
论文作者
论文摘要
我们引入了一种从头弹性图，以从测得的菌株中学习固体的弹性。我们新方法中的深神网络受弹性理论的监督，并且不需要标记的数据进行培训。结果表明，所提出的方法可以准确地学习固体的隐藏弹性，并且在嘈杂和缺失的测量方面非常健壮。基于附近地区的弹性分布，神经网络也可以重建没有测量区域的可能的弹性分布。神经网络了解固体的隐藏弹性随位置的函数，因此可以通过任意分辨率生成弹性图像。此功能用于在本研究中创建超分辨率弹性图像。我们证明，当给出应变和弹性分布时，神经网络也可以学习隐藏的物理。所提出的方法具有各种独特的功能，可以应用于广泛的弹性应用应用程序。
We introduce a de novo elastography method to learn the elasticity of solids from measured strains. The deep neural network in our new method is supervised by the theory of elasticity and does not require labeled data for training. Results show that the proposed method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements. A probable elasticity distribution for areas without measurements may also be reconstructed by the neural network based on the elasticity distribution in nearby regions. The neural network learns the hidden elasticity of solids as a function of positions and thus it can generate elasticity images with an arbitrary resolution. This feature is applied to create super-resolution elasticity images in this study. We demonstrate that the neural network can also learn the hidden physics when strain and elasticity distributions are both given. The proposed method has various unique features and can be applied to a broad range of elastography applications.
