Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks

TL;DR

This paper presents a multi-task neural network framework combining proper orthogonal decomposition and LSTM to predict elastoplastic deformations in real-time with high accuracy and efficiency, significantly outperforming traditional methods.

Contribution

It introduces a novel multi-task learning approach that enhances prediction accuracy and reduces training data requirements for real-time elastoplastic deformation simulation.

Findings

Achieves mean absolute error below 0.40% across variables

Requires only 20 samples to train additional variables effectively

Accelerates computations by approximately a million times compared to finite element analysis

Abstract

This study introduces a surrogate modeling framework merging proper orthogonal decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. Superior to single-task neural networks, this approach achieves a mean absolute error below 0.40\% across various state variables, with the multi-task model showing enhanced generalization by mitigating overfitting through shared layers. Moreover, in our use cases, a pre-trained multi-task model can effectively train additional variables with as few as 20 samples, demonstrating its deep understanding of complex scenarios. This is notably efficient compared to single-task models, which typically require around 100 samples. Significantly faster than traditional finite element analysis, our model accelerates computations by approximately a million times, making it a substantial advancement for real-time predictive modeling in engineering applications. While it necessitates further testing on more intricate models, this framework shows substantial promise in elevating both efficiency and accuracy in engineering applications, particularly for real-time scenarios.