  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.
