论文标题
Viscoelasticnet：物理知情的神经网络框架，用于发现压力和模型选择
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
论文作者
论文摘要
粘弹性流体是表现出粘性和弹性性质的一类流体。建模这种流体需要应力的组成方程，而选择最合适的构成关系可能很困难。我们提出了Viscoelasticnet，这是一种具有物理信息的深度学习框架，该框架使用速度流场选择组成型模型并学习应力场。我们的框架仅需要用于速度场，应力张量的初始\＆边界条件以及压力场的边界条件。使用此信息，我们学习模型参数，压力场和应力张量。 {这项工作考虑}三种常用的非线性粘弹性模型：Oldroyd-B，Giesekus和线性Phan-Thien-Tanner（PTT）。我们证明我们的框架与嘈杂且稀疏的数据效果很好。我们的框架可以与从实验技术等实验技术中获取的速度场结合使用，例如粒子图像速度法，以获得压力\＆应力场和本构方程的模型参数。一旦使用ViscoelasticNet发现了模型，就可以为进一步的应用模拟并建模流体。
Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most appropriate constitutive relationship can be difficult. We present viscoelasticNet, a physics-informed deep learning framework that uses the velocity flow field to select the constitutive model and learn the stress field. Our framework requires data only for the velocity field, initial \& boundary conditions for the stress tensor, and the boundary condition for the pressure field. Using this information, we learn the model parameters, the pressure field, and the stress tensor. {This work considers} three commonly used non-linear viscoelastic models: Oldroyd-B, Giesekus, and linear Phan-Thien-Tanner (PTT). We demonstrate that our framework works well with noisy and sparse data. Our framework can be combined with velocity fields acquired from experimental techniques like particle image velocimetry to get the pressure \& stress fields and model parameters for the constitutive equation. Once the model has been discovered using viscoelasticNet, the fluid can be simulated and modeled for further applications.
