# Stress field prediction in fiber-reinforced composite materials using a deep learning approach

arXiv: https://arxiv.org/abs/2111.05271 · HTML: https://mlsanity.com/paper/2111.05271/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks](https://mlsanity.com/paper/1808.08914/) (2018)
- [A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions](https://mlsanity.com/paper/1805.02784/) (2018)
- [Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization](https://mlsanity.com/paper/2002.07600/) (2020)
- [Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks](https://mlsanity.com/paper/2010.03675/) (2020)
- [An efficient optimization based microstructure reconstruction approach with multiple loss functions](https://mlsanity.com/paper/2102.02407/) (2021)
- [Probabilistic modeling of discrete structural response with application to composite plate penetration models](https://mlsanity.com/paper/2011.11780/) (2020)

## Built on by (most cited first, 23 of 23)

- [A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method](https://mlsanity.com/paper/2206.13103/) (2022)
- [A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains](https://mlsanity.com/paper/2405.12465/) (2024)
- [Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient](https://mlsanity.com/paper/2506.04375/) (2025)
- [Challenges and opportunities for machine learning in multiscale computational modeling](https://mlsanity.com/paper/2303.12261/) (2023)
- [Bayesian neural networks for predicting uncertainty in full-field material response](https://mlsanity.com/paper/2406.14838/) (2024)
- [Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials](https://mlsanity.com/paper/2410.05281/) (2024)
- [A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties](https://mlsanity.com/paper/2407.15665/) (2024)
- [Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model](https://mlsanity.com/paper/2504.14143/) (2025)
- [Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design](https://mlsanity.com/paper/2112.10944/) (2021)
- [Linking Properties to Microstructure in Liquid Metal Embedded Elastomers via Machine Learning](https://mlsanity.com/paper/2208.04146/) (2022)
- [Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process](https://mlsanity.com/paper/2212.05885/) (2022)
- [Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method](https://mlsanity.com/paper/2505.04875/) (2025)
- [A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials](https://mlsanity.com/paper/2603.18225/) (2026)
- [Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field](https://mlsanity.com/paper/2303.16869/) (2023)
- [Crack-Net: Prediction of Crack Propagation in Composites](https://mlsanity.com/paper/2309.13626/) (2023)
- [Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information](https://mlsanity.com/paper/2504.18854/) (2025)
- [Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials](https://mlsanity.com/paper/2505.01438/) (2025)
- [A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts](https://mlsanity.com/paper/2506.08205/) (2025)
- [Deep Micro Solvers for Rough-Wall Stokes Flow in a Heterogeneous Multiscale Method](https://mlsanity.com/paper/2507.13902/) (2025)
- [One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields](https://mlsanity.com/paper/2606.22752/) (2026)
- [Appearance-Preserving Refinement of Generated 3D Assets for Monochromatic Fabrication](https://mlsanity.com/paper/2606.26850/) (2026)
- [Hybrid Unet-Transformer Model for Generating Stress and Strain Fields from Composite Geometrics](https://mlsanity.com/paper/2606.31068/) (2026)
- [Deep Learning-based Mechanical Characterization of Synthetically Generated Plant Fibers under Transverse Compression](https://mlsanity.com/paper/2610.16529/) (2026)

## Abstract (arXiv)

Computational stress analysis is an important step in the design of material systems. Finite element method (FEM) is a standard approach of performing stress analysis of complex material systems. A way to accelerate stress analysis is to replace FEM with a data-driven machine learning based stress analysis approach. In this study, we consider a fiber-reinforced matrix composite material system and we use deep learning tools to find an alternative to the FEM approach for stress field prediction. We first try to predict stress field maps for composite material systems of fixed number of fibers with varying spatial configurations. Specifically, we try to find a mapping between the spatial arrangement of the fibers in the composite material and the corresponding von Mises stress field. This is achieved by using a convolutional neural network (CNN), specifically a U-Net architecture, using true stress maps of systems with same number of fibers as training data. U-Net is a encoder-decoder network which in this study takes in the composite material image as an input and outputs the stress field image which is of the same size as the input image. We perform a robustness analysis by taking different initializations of the training samples to find the sensitivity of the prediction accuracy to the small number of training samples. When the number of fibers in the composite material system is increased for the same volume fraction, a finer finite element mesh discretization is required to represent the geometry accurately. This leads to an increase in the computational cost. Thus, the secondary goal here is to predict the stress field for systems with larger number of fibers with varying spatial configurations using information from the true stress maps of relatively cheaper systems of smaller fiber number.