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Published VersionCC BY V4.0, Open Access
Journal article
An advanced physics-informed neural network-based framework for nonlinear and complex topology optimization
Engineering Structures, Vol.322(Part B), pp.1-13
2025
Abstract
In this present paper, we introduce an advanced Physics-Informed Neural Network (PINN) based Topology Optimization (TO) framework for addressing complex structural design challenges. Traditional applications of PINN-based TO have primarily focused on solving classical compliance minimization problems. However, to demonstrate its broad applicability, PINNs must be utilized to address wide range of TO challenges. Addressing this gap, this study developed an advanced Complete Physics-Informed Neural Network-based Topology Optimization (CPINNTO) framework, offering a viable approach for solving complex and nonlinear TO challenges. To do so, a Deep Energy Method (DEM) PINN structures and Sensitivity-analysis PINN (S-PINN) models are constructed to estimate the structure displacement and deriving objective function, respectively. Moreover, CPINNTO is adapted for various TO challenges including periodic, multi-scale, multi-material and geometrically nonlinear TO problems. The numerical experiments indicate that the CPINNTO is capable of obtaining optimal topologies for complex TO problems without labelled data for PINNs or with no involvement of Finite Element Analysis (FEA). Notably, CPINNTO effectively optimized nonlinear designs under geometrically nonlinear conditions using the proposed Saint Venant-Kirchhoff (SVK) DEM-PINN model. In summary, the proposed CPINNTO framework presents an innovative approach for a wide range of practical applications.
Details
- Title
- An advanced physics-informed neural network-based framework for nonlinear and complex topology optimization
- Authors
- Hyogu Jeong (Author) - Queensland University of TechnologyChanaka P Batuwatta Gamage (Author) - Queensland University of TechnologyJinshuai Bai (Author) - Queensland University of TechnologyCharith Rathnayaka (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringYing Zhou (Author) - Northwestern Polytechnical UniversityYuantong Gu (Corresponding Author) - Queensland University of Technology
- Publication details
- Engineering Structures, Vol.322(Part B), pp.1-13
- Publisher
- Elsevier Ltd
- Date published
- 2025
- DOI
- 10.1016/j.engstruct.2024.119194
- ISSN
- 1873-7323; 0141-0296
- Copyright note
- © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
- Data Availability
- Data will be made available on request.
- Grants
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991084798802621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Engineering, Civil