Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem

Qi Zhang, Yilin Chen, Ziyi Yang, Eric Darve

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.
Original languageEnglish
Title of host publicationMulti-Constitutive Neural Network for Large Deformation Poromechanics Problem
PublisherProceedings of the Machine Learning and the Physical Sciences Workshop, 34th Conference on Neural Information Processing Systems (NeurIPS)
Number of pages6
Publication statusPublished - 11 Dec 2020
Externally publishedYes


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