Work

Local stress fields prediction using global displacement through Fourier neural operators

Year: 2025

Type: article

Abstract: Accurately capturing the stress distribution within complex structures is crucial for reliability and performance, particularly in high-stress environments. Traditional finite element analysis methods require a fine mesh to achieve highly accurate stress fields, which is computationally expensive and time-consuming. This paper presents a novel neural network-based approach that learns the mapping between global displacement values on coarse global elements and local von Mises stress on fine local elements. By training the neural network on a dataset of paired global displacement and local stress fields, our method effectively predicts high-resolution stress distributions from coarse-grained inputs. This approach significantly reduces the computational burden associated with fine mesh finite element analysis, enabling real-time simulations and optimizations. The proposed method has been validated by the test results that demonstrate the ability to accurately predict local stress fields while maintaining computational efficiency.

Source: Journal of Computational Design and Engineering

DOI: 10.1093/jcde/qwaf044

Authors: Minwoo Shin, Minjee Seo, Hayoung Choi, Jaemin Jung, Kyungho Yoon

Institutions: Yonsei University, Yonsei University Mirae Campus, Hyundai Heavy Industries (South Korea), Pohang University of Science and Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,650

Funders: Neurosciences Research Foundation, National Research Foundation, Ministry of Science and ICT, South Korea