DEBOSH: Deep Bayesian Shape Optimization

2021/09/28 by Nikita Durasov, Artem Lukoyanov, Durasov, Nikita +5 · 2 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Artificial Intelligence (cs.AI) #Computer Graphics and Visualization Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Visual Attention and Saliency Detection

paper · pdf · doi:10.48550/arxiv.2109.13337

openalex publication_date 2021/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively. However, to fully explore the shape space, one must often consider shapes deviating significantly from the training set. For these, GNN predictions become unreliable, something that is often ignored. For optimization techniques relying on Gaussian Processes, Bayesian Optimization (BO) addresses this issue by exploiting their ability to assess their own accuracy. Unfortunately, this is harder to do when using neural networks because standard approaches to estimating their uncertainty can entail high computational loads and reduced model accuracy. Hence, we propose a novel uncertainty-based method tailored to shape optimization. It enables effective BO and increases the quality of the resulting shapes beyond that of state-of-the-art approaches.

Citations

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• ShapeNet: An Information-Rich 3D Model Repository
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Cited by

• 3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization
• Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning

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