论文标题
用于大变形门力学问题的多稳定神经网络
Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
论文作者
论文摘要
在本文中，我们研究了具有深神经网络（DNN）的门能力学大型巩固的问题。给定不同的材料特性和不同的加载条件，目标是预测孔隙压力和沉降。我们提出了一种新颖的方法“多稳定神经网络”（MCNN），以便一个模型可以解决几种不同的本构定律。我们引入了一个单热编码向量作为额外的输入向量，该向量用于标记我们希望解决的本构法律。然后，我们构建一个DNN，该DNN将$（\ hat {x}，\ hat {t}）$作为输入以及本构法标签，并输出相应的解决方案。据我们所知，我们第一次只能通过一个培训过程来评估多构法的法律，同时仍然获得良好的准确性。我们发现，在某些情况下，经过培训以解决多个PDE的MCNN优于接受PDE训练的单个神经网络求解器。
In this paper, we study the problem of large-strain consolidation in poromechanics with deep neural networks (DNN). Given different material properties and different loading conditions, the goal is to predict pore pressure and settlement. We propose a novel method "multi-constitutive neural network" (MCNN) such that one model can solve several different constitutive laws. We introduce a one-hot encoding vector as an additional input vector, which is used to label the constitutive law we wish to solve. Then we build a DNN which takes $(\hat{X}, \hat{t})$ as input along with a constitutive law label and outputs the corresponding solution. It is the first time, to our knowledge, that we can evaluate multi-constitutive laws through only one training process while still obtaining good accuracies. We found that MCNN trained to solve multiple PDEs outperforms individual neural network solvers trained with PDE in some cases.
