Global topology optimization neural networks for metasurface design (Conference Presentation)

Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.

Download Full-text

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4044097 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 6

Author(s):

Zhenguo Nie ◽

Haoliang Jiang ◽

Levent Burak Kara

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Topology Optimization ◽

Structural Analysis ◽

Stress Field ◽

Convolutional Neural Networks ◽

Von Mises Stress ◽

Channel Network ◽

Promising Alternative ◽

And Topology

Abstract The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multichannel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available.2

Download Full-text

Accelerating gradient-based topology optimization design with dual-model artificial neural networks

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-020-02770-6 ◽

2020 ◽

Author(s):

Chao Qian ◽

Wenjing Ye

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Topology Optimization ◽

Optimization Design ◽

Dual Model ◽

Gradient Based ◽

Artificial Neural

Download Full-text

Laminated-Object-Manufacturing*/Laminated object manufacturing - A method for the production of topological optimization parts

wt Werkstattstechnik online ◽

10.37544/1436-4980-2016-05-68 ◽

2016 ◽

Vol 106 (05) ◽

pp. 354-359

Author(s):

M. Mottahedi ◽

P. Zahn ◽

A. Lechler ◽

A, Prof. Verl

Keyword(s):

Topology Optimization ◽

Additive Manufacturing ◽

Topological Optimization ◽

Optimal Topology ◽

Manufacturing Method ◽

Laminated Object Manufacturing ◽

Maximum Stiffness ◽

Global Topology ◽

The Cost

Topologisch optimierte Bauteile gestatten maximale Steifigkeit bei minimalem Materialeinsatz. Für die Erzeugung solcher Topologien werden meist Algorithmen eingesetzt, die Fertigungseinschränkungen auf Kosten von optimalen Ergebnissen berücksichtigen und keine variablen Materialdichten zulassen. Dieser Fachartikel stellt ein additives Herstellungsverfahren zur Fertigung global optimaler Topologien vor. Als Ergebnis können mittels der ausgewählten Algorithmen Bauteile mit höherer Steifigkeit hergestellt werden.   The optimal topology of components leads to maximum stiffness with minimum material use. To generate these topologies, normally algorithms are employed that tackle manufacturing limitations at the cost of the optimum. This article introduces an additive manufacturing method to enable the production of global topology optimization results. The findings show that by implementing the selected algorithm the stiffness of the components are higher than what could have been produced by conventional techniques.

Download Full-text