.png){kind=icon}
返回
A Physics-Informed Neural Network-based Topology Optimization (PINNTO) framework for structural optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.engstruct.2022.115484.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Physics-Informed Neural Networks (PINNs) have recently attracted exponentially increasing attention in the field of computational mechanics. This paper proposes a novel topology optimization framework: Physics-Informed Neural Network-based Topology Optimization (PINNTO). Unlike existing machine-learning based topology optimization frameworks, PINNTO employs an energy-based PINN to replace Finite Element Analysis (FEA) in the conventional structural topology optimization, to numerically determine the deformation states, which is a key novelty in the proposed methodology. A supervised neural network that respects governing physical laws defined via partial differential equations is trained to develop the corresponding network without any labelled data, with the intention of solving solid mechanics problems. To assess feasibility and potential of the proposed PINNTO framework, a number of topology-optimization-related case studies have been implemented. The sub-sequent findings illustrate that PINNTO has the ability to attain optimized topologies with neither labelled data nor FEA. In addition, it has the capability to generate comparable designs to those produced by the current successful approaches such as Solid Isotropic Material with Penalization (SIMP). Based on the results of this study, it can also be deduced that PINNTO can acquire optimal topologies for various types of complex domains given that the boundary conditions and loading configurations are correctly imposed for the associated energy-based PINN. Consequently, the proposed PINNTO framework has demonstrated promising capabilities to solve problems under conditions when the usage of FEA is challenged (if not impossible). In summary, the proposed PINNTO framework opens up a new avenue for structural design in this 'data-rich' age.
Keyword:
Topology optimization
Physics informed neural network
Machine learning
Solid mechanics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.4
论文数:
2.1W
被引数:
8.7W
机构
引用论文
PHYSICS-INFORMED NEURAL NETWORKS WITH HARD CONSTRAINTS FOR INVERSE DESIGN\ast用于逆设计的具有硬约束的物理信息神经网络 \ ast
OAAI
OAAI分享收藏Stiffness design of geometrically nonlinear structures using topology optimization基于拓扑优化的几何非线性结构刚度设计
PREAI
分享收藏Influence of Density-Based Topology Optimization Parameters on the Design of Periodic Cellular Materials.png)
MATERIALSOAAI
MATERIALSIF3.2
OAAI分享收藏Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架OAAI
OAAI分享收藏Convergent and mesh-independent solutions for the bi-directional evolutionary structural optimization method双向进化结构优化方法的收敛和网格无关解
PREAI
分享收藏Optimal design of periodic structures using evolutionary topology optimization基于进化拓扑优化的周期性结构优化设计
PREAI
分享收藏
查看更多内容