Multi-fidelity data fusion for inelastic woven composites: Combining recurrent neural networks with transfer learning
Artikel i vetenskaplig tidskrift, 2025

Surrogate deep learning models provide an efficient solution for reducing the computational demands of homogenizing complex meso-scale woven composites to study their elasto-plastic mechanical behaviors. This research introduces a comprehensive framework using transfer learning that combines data from a mean-field homogenization approach with high-fidelity full-field simulations. In a design space characterized by diverse loading conditions and micro-scale constitutive material properties, the goal is to address the challenges of generating sufficient datasets for training a data-hungry gated recurrent neural network (GRU). Multiple datasets of varying precision are generated and used, containing multi-axial stress–strain responses under two load types: random walking and proportional cyclic loading. Moreover, this study emphasizes the importance of temporal correlations in the dataset, which align with the physically path-dependent behavior of most non-linear materials, and demonstrates that temporal correlations are crucial for training time-series models. These correlations also provide the foundation for data augmentation using a linear interpolation technique within time-series stress analyses, enabling accurate predictions of homogenized meso-scale stresses based on strain trajectories and microstructural properties. Results demonstrate that integrating transfer learning with neural networks successfully incorporates a limited number of high-fidelity data with more accessible but low-fidelity data. With this framework, surrogate models for predicting the complex behavior of woven composites will be accurate and efficient, marking an important advancement in material modeling.

Fast-Fourier transform

Transfer-learning

Meso-scale

Plasticity

Gated recurrent networks

Woven composites

Författare

Ehsan Ghane

Göteborgs universitet

Martin Fagerström

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Mohsen Mirkhalaf

Göteborgs universitet

Composites Science and Technology

0266-3538 (ISSN)

Vol. 267 111163

LIGHTer Academy etapp 4

VINNOVA (2023-01937), 2023-10-01 -- 2025-10-22.

Ämneskategorier (SSIF 2025)

Teknisk mekanik

DOI

10.1016/j.compscitech.2025.111163

Relaterade dataset

Multi-fidelity-Data-Fusion-for-In-elastic-Woven-Composites [dataset]

URI: https://github.com/EsanGhaneh/Multi-fidelity-Data-Fusion-for-In-elastic-Woven-Composites.git

Mer information

Senast uppdaterat

2025-05-09