Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction

• URL: http://arxiv.org/abs/2406.01996v1
• Date: Tue, 4 Jun 2024 06:27:48 GMT
• Title: Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction
• Authors: Jangseop Park, Namwoo Kang,
• Abstract summary: In engineering design, surrogate models are widely employed to replace computationally expensive simulations. We propose a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based surrogate model. Our framework determines the optimal size of mesh elements through Bayesian optimization, resulting in a high-accuracy surrogate model.
• Score: 1.6574413179773761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In engineering design, surrogate models are widely employed to replace computationally expensive simulations by leveraging design variables and geometric parameters from computer-aided design (CAD) models. However, these models often lose critical information when simplified to lower dimensions and face challenges in parameter definition, especially with the complex 3D shapes commonly found in industrial datasets. To address these limitations, we propose a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based surrogate model that predicts engineering performance by directly learning geometric features from CAD using mesh representation. Our framework determines the optimal size of mesh elements through Bayesian optimization, resulting in a high-accuracy surrogate model. Additionally, it effectively handles the irregular and complex structures of 3D CADs, which differ significantly from the regular and uniform pixel structures of 2D images typically used in deep learning. Experimental results demonstrate that the quality of the mesh significantly impacts the prediction accuracy of the surrogate model, with an optimally sized mesh achieving superior performance. We compare the performance of models based on various 3D representations such as voxel, point cloud, and graph, and evaluate the computational costs of Monte Carlo simulation and Bayesian optimization methods to find the optimal mesh size. We anticipate that our proposed framework has the potential to be applied to mesh-based simulations across various engineering fields, leveraging physics-based information commonly used in computer-aided engineering.

Related papers

• DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations [2.300471499347615]
  DoMINO is a point cloudbased machine learning model that uses local geometric information to predict flow fields on discrete points. DoMINO is validated for the automotive aerodynamics use case using the DrivAerML dataset.
  arXiv  Detail & Related papers  (2025-01-23T03:28:10Z)
• ConvMesh: Reimagining Mesh Quality Through Convex Optimization [55.2480439325792]
  This research introduces a convex optimization programming called disciplined convex programming to enhance existing meshes. By focusing on a sparse set of point clouds from both the original and target meshes, this method demonstrates significant improvements in mesh quality with minimal data requirements.
  arXiv  Detail & Related papers  (2024-12-11T15:48:25Z)
• Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics [50.23625950905638]
  We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment. Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region.
  arXiv  Detail & Related papers  (2024-12-11T08:27:33Z)
• GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation [65.33726478659304]
  We introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D structure and do not utilize explicit geometric relationships between 3D and 2D images. GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms.
  arXiv  Detail & Related papers  (2024-06-21T17:49:31Z)
• Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations [49.173541207550485]
  Adaptive Meshing By Expert Reconstruction (AMBER) is an imitation learning problem. AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh. We experimentally validate AMBER on 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline.
  arXiv  Detail & Related papers  (2024-06-20T10:01:22Z)
• Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks [8.736819316856748]
  This paper presents a machine learning-based scheme that utilize Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. We introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, PointNet++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method.
  arXiv  Detail & Related papers  (2023-08-14T14:39:13Z)
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
  This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
  arXiv  Detail & Related papers  (2023-08-10T06:40:19Z)
• Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes [0.0]
  Mesh-based numerical solvers are an important part in many design tool chains. Machine Learning based surrogate models are fast in predicting approximate solutions but often lack accuracy. This paper scales a state-of-the-art surrogate model from the domain of graph-based machine learning to industry-relevant mesh sizes.
  arXiv  Detail & Related papers  (2023-07-25T15:49:25Z)
• Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis [17.920305227880245]
  Our paper formulates triple vision tasks in a consistent manner using approximate analysis-by-synthesis. We show that our analysis-by-synthesis is much more robust than conventional neural networks when evaluated on real-world images.
  arXiv  Detail & Related papers  (2023-05-31T18:45:02Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design [0.20315704654772412]
  We develop graph neural networks (GNNs) as fast surrogate models for physics simulation. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications.
  arXiv  Detail & Related papers  (2023-02-01T16:23:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.