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contributor authorFerguson, Kevin
contributor authorChen, Yu-hsuan
contributor authorChen, Yiming
contributor authorGillman, Andrew
contributor authorHardin, James
contributor authorBurak Kara, Levent
date accessioned2025-04-21T10:38:50Z
date available2025-04-21T10:38:50Z
date copyright11/18/2024 12:00:00 AM
date issued2024
identifier issn1050-0472
identifier othermd_147_4_041701.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306616
description abstractMachine-learned surrogate models to accelerate lengthy computer simulations are becoming increasingly important as engineers look to streamline the product design cycle. In many cases, these approaches offer the ability to predict relevant quantities throughout a geometry, but place constraints on the form of the input data. In a world of diverse data types, a preferred approach would not restrict the input to a particular structure. In this paper, we propose topology-agnostic graph U-Net (TAG U-Net), a graph convolutional network that can be trained to input any mesh or graph structure and output a prediction of a target scalar field at each node. The model constructs coarsened versions of each input graph and performs a set of convolution and pooling operations to predict the node-wise outputs on the original graph. By training on a diverse set of shapes, the model can make strong predictions, even for shapes unlike those seen during training. A 3D additive manufacturing dataset is presented, containing laser powder bed fusion simulation results for thousands of parts. The model is demonstrated on this dataset, and it performs well, predicting both 2D and 3D scalar fields with a median R2>0.85 on test geometries.
publisherThe American Society of Mechanical Engineers (ASME)
titleTopology-Agnostic Graph U-Nets for Scalar Field Prediction on Unstructured Meshes
typeJournal Paper
journal volume147
journal issue4
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4066960
journal fristpage41701-1
journal lastpage41701-12
page12
treeJournal of Mechanical Design:;2024:;volume( 147 ):;issue: 004
contenttypeFulltext


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