Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data

• URL: http://arxiv.org/abs/2309.04499v1
• Date: Fri, 8 Sep 2023 00:26:44 GMT
• Title: Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data
• Authors: Seungyeon Shin, Namwoo Kang
• Abstract summary: We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels.
• Score: 2.306144660547256
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-based engineering performance prediction models have been proposed to accelerate design optimization. However, they only guarantee predictions on training data and may be inaccurate when applied to new domain data. In particular, 3D design data have complex features, which means domains with various distributions exist. Thus, the utilization of deep learning has limitations due to the heavy data collection and training burdens. We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. It is specialized for deep learning-based engineering performance predictions. Domain-invariant features can be extracted through an adversarial training strategy by using hypothesis discrepancy, and a multi-output regression task can be performed with the extracted features to predict the engineering performance. In particular, we present a source instance weighting method suitable for 3D design data to avoid negative transfers. The developed bi-weighting strategy based on the geometry features and engineering performance of engineering structures is incorporated into the training process. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels. This mechanism can reduce the target risk for unlabeled target domains on the basis of weighted multi-source domain knowledge and can efficiently replace conventional finite element analysis.

Related papers

• OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Prediction [5.285847977231642]
  3D semantic occupancy prediction is crucial for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features. We propose OccLoff, a framework that Learns to optimize Feature Fusion for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-11-06T06:34:27Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Generative Adversarial Networks for Imputing Sparse Learning Performance [3.0350058108125646]
  This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space. This finding enhances comprehensive learning data modeling and analytics in AI-based education.
  arXiv  Detail & Related papers  (2024-07-26T17:09:48Z)
• Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction [1.6574413179773761]
  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.
  arXiv  Detail & Related papers  (2024-06-04T06:27:48Z)
• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design [63.30166298698985]
  Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates. Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, have greatly advanced the field of structure-based drug design.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools [96.38972082580294]
  DiffSkill is a novel framework that uses a differentiable physics simulator for skill abstraction to solve deformable object manipulation tasks. In particular, we first obtain short-horizon skills using individual tools from a gradient-based simulator. We then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input.
  arXiv  Detail & Related papers  (2022-03-31T17:59:38Z)
• Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data [0.0]
  We present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation.
  arXiv  Detail & Related papers  (2022-03-17T09:54:22Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data [68.8204255655161]
  An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way. Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
  arXiv  Detail & Related papers  (2020-12-11T14:33:27Z)
• A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics [3.8698051494433043]
  We propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters. We validate our framework on a microelectronics use-case using the recently published PCB scans dataset.
  arXiv  Detail & Related papers  (2020-02-25T15:54:33Z)

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.