# StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials

arXiv: https://arxiv.org/abs/2011.10227 · HTML: https://mlsanity.com/paper/2011.10227/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Large Language Model Agent as a Mechanical Designer](https://mlsanity.com/paper/2404.17525/) (2024)
- [A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks](https://mlsanity.com/paper/2211.12459/) (2022)
- [A Foundation Model for Material Fracture Prediction](https://mlsanity.com/paper/2507.23077/) (2025)
- [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)
- [Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading](https://mlsanity.com/paper/2309.13022/) (2023)
- [Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning](https://mlsanity.com/paper/2406.02582/) (2024)
- [Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale](https://mlsanity.com/paper/2603.13445/) (2026)
- [Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction](https://mlsanity.com/paper/2607.16238/) (2026)
- [DeepFEAv2: Deep Learning for Transient Finite Element Analysis Beyond Structured Meshes](https://mlsanity.com/paper/2609.26426/) (2026)
- [Deep Learning-based Mechanical Characterization of Synthetically Generated Plant Fibers under Transverse Compression](https://mlsanity.com/paper/2610.16529/) (2026)

## Abstract (arXiv)

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a novel deep learning model, "StressNet," is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an average MAPE of 2% relative to test data.