Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
Not on the map. Why?
Abstract (arXiv)
Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Graph neural network (GNN) is a particular type of neural network which processes data that can be represented as graphs. This allows for efficient representation of complex geometries that can change during conceptual design of a structure or a product. In this study, we propose a novel graph embedding technique for efficient representation of 3D stiffened panels by considering separate plate domains as vertices. This approach is considered using Graph Sampling and Aggregation (GraphSAGE) to predict stress distributions in stiffened panels with varying geometries. A comparison between a finite-element-vertex graph representation is conducted to demonstrate the effectiveness of the proposed approach. A comprehensive parametric study is performed to examine the effect of structural geometry on the prediction performance. Our results demonstrate the immense potential of graph neural networks with the proposed graph embedding method as robust reduced-order models for 3D structures.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 65 references that are in the map. First posted on arXiv 22 Sep 2023.
Builds on
18 of 65 references.
References · 18
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2022Generative Adversarial Networkscited by 5,603
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2017Neural Message Passing for Quantum Chemistrycited by 3,460
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
- 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
- 2017Scene Graph Generation by Iterative Message Passingcited by 462
- 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
6 more
- 2019A Gentle Introduction to Deep Learning for Graphscited by 107
- 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
- 20183D Topology Optimization using Convolutional Neural Networkscited by 27
- 2020StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materialscited by 9
- 2021Towards Reusable Surrogate Models: Graph-Based Transfer Learning on Trussescited by 7
- 2022Deep convolutional neural network for shape optimization using level-set approachcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.