Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
This addresses the need for efficient simulation of complex poromechanical systems in engineering, though it is incremental as it builds on existing neural network methods for PDEs.
The paper tackles the problem of predicting pore pressure and settlement in large-strain consolidation poromechanics with varying material properties and loading conditions, proposing a multi-constitutive neural network (MCNN) that uses a one-hot encoding label to solve multiple constitutive laws in one training process, achieving good accuracies and outperforming individual neural network solvers in some cases.
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.