检索结果-内蒙古大学图书馆

Data-driven topology optimization using a multitask conditional variational autoencoder with persistent homology

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2024年第7期67卷 133-133页

作者： Sugai, Tomotaka Shintani, Kohei Yamada, Takayuki Univ Tokyo Grad Sch Engn 2-11-6 YayoiBunkyo Ku Tokyo 1138656 Japan

Topology optimization is crucial for the mechanical design of vehicles and aircraft, allowing changes in the shape of structures and the placement of features. Recent advances have integrated deep generative models, particularly convolutional neural networks, to streamline this *** streamline this process. However, these models struggle to preserve subtle structural features. To overcome these limitations, this study introduced a generative model adept at identifying the topological features inherent in real shapes, such as connectivity and holes, to enhance the effectiveness of topology optimization. A conditional variational autoencoder (CVAE) was employed to predict both the shape and compliance simultaneously. This model, CVAE with persistent homology, generates optimal material distributions by considering topological properties. The learning process introduced a term that minimizes the difference in topological features between true and reconstructed shapes. The proposed model can generate optimal material distributions by considering topological properties, eliminating the need for iterative calculations. This approach was validated using two numerical examples. The accuracy of the generated material distributions was compared with conventional methods using the mean-squared error. An average improvement in accuracy of approximately 36.85% was observed across the two results. This confirms that shapes considering compliance and connectivity can be accurately predicted.

关键词： Topology optimization conditional variational autoencoder Persistent homology Topological features

来源：评论

学校读者我要写书评

暂无评论

Molecular generative model based on conditional variational autoencoder for de novo molecular design

引用

JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 31-31页

作者： Lim, Jaechang Ryu, Seongok Kim, Jin Woo Kim, Woo Youn Korea Adv Inst Sci & Technol Dept Chem 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KI Artificial Intelligence 291 Daehak Ro Daejeon 34141 South Korea

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

关键词： Molecular design conditional variational autoencoder Deep learning

来源：评论

学校读者我要写书评

暂无评论

A conditional variational autoencoder Algorithm for Reconstructing Defect Data of Magnetic Flux Leakage

A Conditional Variational Autoencoder Algorithm for Reconstr...

引用

第32届中国控制与决策会议

作者： Senxiang Lu Jing Wu Jianan Zhang School of Information Science and Engineering Northeastern University Liaoning Provincial Institute of Measurement

Magnetic flux leakage（MFL） testing is widely used in pipeline nondestructive *** acquisition quality of MFL signal is an important prerequisite for the accuracy of pipeline detection and ***,data loss often occurs in the process of detection,due to the interference of external environmental ***,it is important to reconstruct the missing *** paper analyzes four types of MFL data loss,and presents a conditional variational autoencoder algorithm to reconstruct defect data of MFL for these types of *** algorithm effectiveness is tested by comparing with traditional variational autoencoder *** results demonstrate that the proposed method can improve the accuracy of reconstructing defect data loss.

关键词： Magnetic Flux Leakage Data Loss conditional variational autoencoder Defect Data Reconstructing

来源：评论

学校读者我要写书评

暂无评论

Optimization of force-field potential parameters using conditional variational autoencoder

引用

SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS 2023年第1期3卷

作者： Matsunoshita, Koki Yamaguchi, Yudai Hamaie, Masato Horibe, Motoki Tanibata, Naoto Takeda, Hayami Nakayama, Masanobu Karasuyama, Masayuki Kobayashi, Ryo Nagoya Inst Technol Dept Adv Ceram Nagoya Aichi 4668555 Japan Nagoya Inst Technol Dept Comp Sci Nagoya Japan Nagoya Inst Technol Dept Appl Phys Nagoya Aichi 4668555 Japan

Owing to their high-speed, force-field (FF) calculations for inorganic solid-state materials using the parametric potential have been widely employed as an effective tool for high-throughput calculations such as exhaustive property evaluation of material databases and/or calculations for models containing tens of thousands of atoms or more, including amorphous and grain boundary models. However, the accuracy of such calculations strongly depends on the choice of the FF parameters. Today, compounds containing three or more elements are targeted in the research and development of functional materials, in which case the number of parameters often exceeds 10 dimensions. This significantly increases the search space, making it difficult to determine the parameters rationally. To resolve this issue, we previously reported an FF parameter determination approach based on meta-heuristics (APL Materials, 8, 081111 (2020)). In this study, we further investigated a methodology to efficiently determine the FF parameters using a conditional variational autoencoder (CVAE), a type of deep learning method, which can reduce the dimension of the input parameters and distribute them in a probabilistic manner in the latent space. As a demonstration, we applied the method to the optimization of 11 FF parameters for an argyrodite-type Li7PS6 material, which has attracted considerable attention as a solid electrolyte for all-solid-state batteries. The results confirmed that the proposed approach can generate valid FFs that are highly consistent with the results of first-principles calculations, even when using a limited set of meta-heuristics-generated FF parameters.

关键词： Force-field parameters cuckoo search conditional variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Machine learning-based mechanical performance prediction and design of lattice structures

引用

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2025年 294卷

作者： Liu, Yifan Huang, Wei Wang, Zhiyong Zhang, Jie Liu, Jiayi Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore Hubei Key Lab Naval Architecture & Ocean Engn Hydr Wuhan 430074 Peoples R China

A data-driven framework is proposed that integrates automated geometric modeling and high-throughput finite element simulations to enable both forward prediction and inverse design of lattice unit cells. By establishing an efficient workflow, a substantial numerical simulation database is generated encompassing diverse truss-based topologies, the fidelity of which is corroborated by 3D-printed prototypes and quasi-static compression tests. A specially designed network is trained to predict elastic modulus, demonstrating improved accuracy (MSE = 0.046, R > 0.994) and reduced overfitting compared to graph neural network (GNN). Building upon this predictive model, a conditional variational autoencoder (C-VAE) is introduced that learns a low-dimensional latent space simultaneously conditioned on topological features and mechanical performance. Subsequent dimensionality reduction and clustering analyses, utilizing principal component analysis (PCA) and K-means algorithms, elucidated intrinsic correlations between rod connectivity and structural stiffness. Ultimately, by coupling the C-VAE with the predictive model, a high-throughput inverse design strategy is realized, enabling the fabrication of unit cells that achieve the prescribed elastic modulus with remarkable fidelity (design error < 2 %), and with a computational design time on the order of 10 s. And the design speed is approximately 66 times faster than that of traditional topology optimization methods. This paradigm significantly accelerates the design of 3D-printed architected materials, offering a promising and data-efficient approach for exploring complex structural geometries.

关键词： Machine learning Inverse design Lattice unit cell 3D-printed architected materials Elastic modulus conditional variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Improving Local Fidelity and Interpretability of LIME by Replacing Only the Sampling Process With CVAE

引用

IEEE ACCESS 2025年 13卷 53084-53099页

作者： Yasui, Daisuke Sato, Hiroshi Natl Def Acad Dept Comp Sci Yokosuka Kanagawa 2390811 Japan

Knowing the basis of decisions is essential for using Machine Learning (ML) in various regions, such as medical diagnosis, finance, and organizational decision-making. The Local Interpretable Model agnostic Explanation (LIME) algorithm is an Explainable AI (XAI) method that can be applied to many black -box models. However, there is a problem in that the local fidelity of the interpretable model decreases. Therefore, local fidelity must be increased, and the more interpretability of the local surrogate model, the better. Moreover, the fewer the changes, the easier it is to apply them to other studies and the more significant the contribution to the XAI area. Therefore, there are studies that improve fidelity by changing only the sampling. However, no studies have been identified that improve local fidelity and interpretability by modifying only the sampling. To this end, we use a conditional variational autoencoder (CVAE) to modify the sampling of LIME in the classification task to improve local fidelity and interpretability. We conducted a study to improve local fidelity and interpretability by modifying only the sampling part and clarifying the mechanism behind this improvement. Results show that the proposed method improves local fidelity while increasing interpretability compared to existing methods and the factors contributing to this improvement. We also conducted experiments on stability, stability against noise, and execution time, and confirmed that the proposed method performs well, although not as well as methods specific to these issues. Our code is publicly released on GitHub (https://***/YasuiDaisuke/***) under the Apache 2.0 License.

关键词： Closed box autoencoders Training data Predictive models Linear regression Hands Adaptation models Training Stability criteria Neural networks LIME local fidelity interpretability conditional variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

A Specialized variational autoencoder for Cost-Efficient Pedestrian Trajectory Prediction

引用

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年

作者： Li, Dongchen Lin, Zhimao Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

The prediction of pedestrian trajectories represents a crucial and widely discussed topic in the field of AI-driven traffic scenarios. The prediction of pedestrian trajectories is constrained by two factors. First, pedestrians do not have the same traffic rule constraints as vehicles. Second, the computational power of in-vehicle systems is limited. This renders the application of traditional methods challenging. Previous methods have been observed to utilize redundant information, which can result in feature imbalance and the potential for model overfitting. In light of these limitations, we propose a lightweight conditional variational autoencoder model with post-process (L-CVAE-P) for pedestrian prediction scenarios. The L-CVAE-P focuses on the efficient interaction of multidimensional features to achieve a comprehensive enhancement of the model for real-world use. The model is tested on two public datasets and achieved state-of-the-art performance, while maintaining efficiency. The experimental results demonstrate that our work has developed and optimized a pedestrian trajectory prediction model for practical applications. (c) 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： pedestrian trajectory conditional variational autoencoder temporal sequence prediction scenarios interaction multi-dimensional feature balance

来源：评论

学校读者我要写书评

暂无评论

conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

引用

WIND ENERGY 2021年第10期24卷 1122-1139页

作者： Mylonas, Charilaos Abdallah, Imad Chatzi, Eleni Fed Inst Technol Zurich Chair Struct Mech & Monitoring Zurich Switzerland

Wind turbine fatigue estimation is based on time-consuming Monte Carlo simulations for various wind conditions, followed by cycle-counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite-element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non-linear mechanics, and control. A variational Auto-Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross-section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind-field measurement statistics, such as mean hub-height wind speed, standard deviation of hub-height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long-term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.

关键词： blade root fatigue conditional variational autoencoder CVAE deep generative models high dimensional simulation outputs SCADA uncertainty quantification wind turbine blade

来源：评论

学校读者我要写书评

暂无评论

Generalized zero-shot domain adaptation via coupled conditional variational autoencoders

引用

NEURAL NETWORKS 2023年 163卷 40-52页

作者： Wang, Qian Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classification problems, domain adaptation has been studied under the assumption all classes are available in the target domain regardless of the annotations. However, a common situation where only a subset of classes in the target domain are available has not attracted much attention. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source -domain samples as semantic representations for zero-shot learning. For this novel problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To solve this problem, we present a novel Coupled conditional variational autoencoder (CCVAE) which can generate synthetic target-domain image features for unseen classes from real images in the source domain. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Generalized zero -shot learning Domain adaptation conditional variational autoencoder Generalized zero-shot domain adaptation

来源：评论

学校读者我要写书评

暂无评论

Design Space Exploration and Explanation via conditional variational autoencoders in Meta-Model-Based Conceptual Design of Pedestrian Bridges

引用

AUTOMATION IN CONSTRUCTION 2024年 163卷

作者： Balmer, Vera Kuhn, Sophia V. Bischof, Rafael Salamanca, Luis Kaufmann, Walter Perez-Cruz, Fernando Kraus, Michael A. Swiss Fed Inst Technol Inst Struct Engn Stefano Franscini Pl 5 CH-8093 Zurich Switzerland Swiss Fed Inst Technol Ctr Augmented Computat Design Architecture Engn & Wolfgang Pauli Str 27 CH-8093 Zurich Switzerland Swiss Data Sci Ctr SDSC Andreasstr 5 CH-8092 Zurich Switzerland Univ Str 6 CH-8006 Zurich Switzerland

Today, engineers rely on conventional iterative (often manual) techniques for conceptual design. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive, leaving room for improvement. This paper provides a design exploration and explanation framework to augment the designer via a conditional variational autoencoder (CVAE), which serves as a forward performance predictor as well as an inverse design generator conditioned on a set of performance requests. Hence, the CVAE overcomes the limitations of traditional iterative techniques by learning a differentiable mapping for a highly nonlinear design space, thus enabling sensitivity analysis. These methods allow for informing designers about (i) relations of the model between features and performances and (ii) structural improvements under user-defined objectives. The framework is tested on a case-study and proves its potential to serve as a future co-pilot for conceptual design studies of diverse civil structures and beyond.

关键词： Computational design Design space exploration Generative AI conditional variational autoencoder Explainable AI Pedestrian bridge

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：