An analysis of functionally graded thin-walled beams using physics-informed neural networks

被引：4

作者：

Trinh, Duy T. N. ^{[1
]}

Luong, Khang A. ^{[1
]}

Lee, Jaehong ^{[1
]}

机构：

[1] Sejong Univ, Deep Learning Architectural Res Ctr, 209 Neungdong Ro, Seoul 05006, South Korea

来源：

ENGINEERING STRUCTURES | 2024年 / 301卷

基金：

新加坡国家研究基金会;

关键词：

Thin-walled structures; Physics-informed neural networks; Functionally graded materials;

D O I：

10.1016/j.engstruct.2023.117290

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

In this paper, a physics-informed neural network (PINN) analytical modeling of functionally graded thin-walled beams is proposed with respect to doubly-symmetric I-and channel-sections. In this regard, the energy-based PINN approach is applied to determine the vertical displacements and angles of twist of the beams.Unidirectional along with contour direction and bi-directional material distributions in axial and through-thickness directions are utilized to validate the effectiveness of the proposed approach. Several numerical examples are studied, showing good agreements with closed-form solutions.

引用

页数：14

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