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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Modeling History Effects in Materials via a Recursive Neural Network

Author(s)

Greg Kenning, Jean-Briac le Graverend

On-Site Speaker (Planned)

Jean-Briac le Graverend

Abstract Scope

In the past, machine-learning models have been used to predict how materials deform under different loading conditions. However, while these models have yielded some promising results, they all lack one key feature: accounting for history effects. Deformation histories affect mechanical responses and are nonetheless poorly taken into account in current modeling efforts. The currently-used machine learning models lack, so far, a long-term memory component to consider history effects, thereby missing a critical piece to improve predictive capabilities. We propose to use a Recursive Neural Network (RNN) called Long Short-Term Memory (LSTM) - a specialized architecture within RNNs that was designed to address the vanishing/exploding gradient problem common when modeling long-range dependencies. The Pytorch Neural Network Software is employed and tested using experimental data on memory effects reported by Chaboche on 316 stainless steel during cyclic loading.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, Mechanical Properties

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