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Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling

GEOPHYSICS 2025年第2期90卷 E41-E50页

作者： Pereira, Joao Lino Azevedo, Leonardo Univ Lisbon Inst Super Tecn CERENA DER Lisbon Portugal

Geophysical methods, such as electrical resistivity tomography (ERT), can be used to image near-surface electrical resistivity, as field measurements depend on subsurface porosity, water saturation, and fluid salinity. ERT has been widely applied to investigate mineral and groundwater resources and in archaeological, environmental, and engineering studies. The prediction of subsurface electrical conductivity from ERT data requires solving a geophysical inverse problem. For near-surface characterization studies, this is often accomplished with deterministic inverse methods. These methods linearize the problem around an initial solution, and their smoothness depends on an imposed a priori spatial regularization term. Depending on this parameterization, these methodologies might struggle to capture the natural variability of the subsurface. Moreover, deterministic solutions have limited capabilities for uncertainty assessment. In contrast, stochastic inverse methods can assess uncertainties by predicting multiple model realizations that fit the recorded ERT data similarly. However, they are often more computationally expensive than deterministic solutions. Deep-learning algorithms based on deep generative models have been used to reparameterize model and data spaces into low- dimensional domains and efficiently solve geophysical inverse problems. However, within this context, uncertainty assessment is challenging. We develop a deep convolutional variational autoencoder (VAE) coupled with stochastic adaptive optimization to perform stochastic ERT inversion. Geostatistical simulations of electrical resistivity are used as the training data set of the VAE. After training, the VAE generates electrical resistivity models that reproduce the statistics and spatial continuity patterns of the training data set. Then, the VAE latent space is iteratively perturbed and updated with adaptive stochastic sampling based on the misfit between observed and predicted ERT data. Our method

关键词： ERT inversion variational autoencoder

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Inverse design of ventilated acoustic resonators using a sound transmission loss-encoded variational autoencoder

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2025年第2期39卷 509-518页

作者： Song, Jin Yeong Hwang, Seok Hyeon Cho, Min Woo Ko, Keon Kim, Baekgyu Song, Kyungjun Park, Sang Min Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Ventilated acoustic resonators (VARs) for simultaneous sound attenuation and ventilation have presented a unique challenge in acoustics. Traditional methods for designing VARs are limited by their reliance on human intuition and extensive computational resources. This study proposes a novel sound transmission loss-encoded variational autoencoder (STL-VAE) for the inverse design of ventilated acoustic resonators (VARs). The STL-VAE model overcomes these limitations by encoding the target sound transmission loss (STL) into a latent space, enabling the generation of VAR designs that achieve broadband sound attenuation. STL-VAE significantly reduces the mean squared error (MSE) between the target STL and the generated VAR designs, outperforming the best designs from the training dataset by over 100fold. The proposed method offers a highly efficient and accurate approach for designing complex acoustic metamaterials with applications in urban and industrial noise mitigation.

关键词： Deep generative model Inverse design Acoustic resonator Acoustic metamaterial variational autoencoder

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Dual-granularity multi-instance multi-label learning with variational autoencoder

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KNOWLEDGE-BASED SYSTEMS 2025年 316卷

作者： Wang, Meixia Zhao, Yuhai Wang, Yejiang Huang, Miaomiao Liu, Xuze Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Multi-instance multi-label learning (MIML) is a weakly supervised approach that models relationships between complex objects and multiple labels, where each object is represented as a bag of instances. A key advantage of MIML is its ability to perform both bag-level and instance-level multi-label predictions, relying solely on bag-level labels. However, a significant performance gap persists between instance-level MIML algorithms and fully supervised learning approaches due to the lack of instance-level labels. Existing MIML algorithms address this challenge by treating bag labels as ambiguous and attempting to reduce supervision imprecision. Moreover, they often assume that instances are independent and identically distributed (i.i.d.) and rely on prior knowledge to learn label correlations, which is impractical in real-world scenarios. To address these challenges, we propose MIMLVAE, a novel dual-granularity MIML algorithm based on a variational autoencoder. MIMLVAE employs a graph attention network to dynamically capture label correlations and instance dependencies, eliminating the i.i.d. assumption and prior knowledge. By treating all instances within a bag equally, it infers effective bag-level and instance-level representations for dual-granularity prediction. At the same time, the label encoder captures label-specific prototype representations, facilitating prototype-based classification at both the bag and instance levels without requiring label disambiguation. Furthermore, MIMLVAE integrates Gaussian mixture model into the shared latent space of features and labels, mitigating posterior collapse and over-regularization. Experiments on six standard MIML datasets demonstrate that MIMLVAE significantly outperforms state-of-the-art methods in both bag-level and instance-level multi-label classification tasks.

关键词： variational autoencoder Correlation learning Prototype classifier

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Attenuation Tomography Using Low-Frequency Ultrasound With variational autoencoder for Thorax Imaging: Experimental Study

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第1期72卷 238-248页

作者： Zhang, Tong Shi, Haokang Lin, Zhichao Cao, Yeyu Guo, Rui Zhang, Haolin Li, Maokun Yang, Fan Xu, Shenheng Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Inst Precis Med Dept Elect Engn Beijing 100084 Peoples R China Johns Hopkins Univ Whiting Sch Engn Dept Comp Sci Baltimore MD 21218 USA Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China

In this paper, we introduce a novel inversion methodology employing the variational autoencoder (VAE) for human thorax attenuation tomography using low-frequency ultrasound. The VAE is trained to assimilate the structural priors of the human thorax, utilizing training samples generated from computed tomography (CT) scans. This approach enables the compression of high-dimensional attenuation distributions into a lower-dimensional latent space. During the inversion process, the latent code is optimized, and then the reconstructed model is generated by the decoder of the VAE. This process can effectively integrate prior information of the domain of interest (DOI) into the inversion through coding and decoding, which would mitigate the ill-posedness of the inverse problem and facilitate better outcomes. Our method demonstrates robust generalization capabilities and noise resilience in numerical simulations, outperforming the conventional pixel-based Gauss-Newton method. Human subject experiment further corroborates the effectiveness of our approach. This is also the first experimental validation of the feasibility of low-frequency ultrasound functional imaging of the human thorax. Although the current study presents certain limitations, it underscores the potential of low-frequency ultrasound in the continuous monitoring of the human respiratory system.

关键词： Thorax Ultrasonic imaging Imaging Attenuation Codes Decoding Training Attenuation tomography low-frequency ultrasound respiratory system monitoring thorax functional imaging variational autoencoder

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DHHNN: A Dynamic Hypergraph Hyperbolic Neural Network based on variational autoencoder for multimodal data integration and node classification

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INFORMATION FUSION 2025年 119卷

作者： Mei, Zhangyu Bi, Xiao Li, Dianguo Xia, Wen Yang, Fan Wu, Hao Shandong Univ Sch Software Jinan 250100 Shandong Peoples R China Qufu Normal Univ Sch Comp Sci Rizhao 276827 Shandong Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China Shandong Univ Hosp 2 Dept Pediat Surg Jinan 250033 Shandong Peoples R China Shandong Univ Qilu Hosp Dept Neurol Jinan 250012 Shandong Peoples R China Shandong Univ Cheeloo Coll Med Sch Publ Hlth Dept Biostat Jinan 250000 Shandong Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Guangdong Peoples R China

In recent years, the integration of hyperbolic geometry with Graph Neural Networks (GNNs) has garnered significant attention due to its effectiveness in capturing complex hierarchical structures, particularly within real-world graphs and scale-free networks. Although hyperbolic neural networks have shown strong performance across various domains, most existing models rely on static graph structures, limiting their adaptability to dynamic data. Previous studies have primarily focused on improving the modeling capacity of hyperbolic space for latent space data during training, often neglecting the preservation of high-order intrinsic features before training. To address this, we propose a novel Dynamic Hypergraph Hyperbolic Neural Network (DHHNN) based on a variational autoencoder for multimodal data integration. This model combines the advantages of hyperbolic geometry, dynamic hypergraphs, and the self-attention mechanism to enhance multimodal data representation learning. DHHNN introduces a dynamic hypergraph framework that continuously adjusts the relationships between hypernodes and hyperedges during training, effectively capturing higher-order dependencies within complex networks. Furthermore, the self-attention mechanism dynamically regulates the dependency levels between hypernodes and hyperedges, enhancing the model's ability to capture long-range dependencies and complex feature interactions. Leveraging the negative curvature of hyperbolic space, DHHNN compactly and accurately represents complex scale-free networks. Experimental results on seven benchmark datasets and latent space visualizations demonstrate that DHHNN provides compact and effective data representation, outperforming existing models and achieving state-of-the-art performance in node classification tasks.

关键词： Hyperbolic hypergraph Dynamic hypergraph variational autoencoder

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An extended variational autoencoder for cross-subject electromyograph gesture recognition

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 99卷

作者： Zhang, Zhen Ming, Yuewei Shen, Quming Wang, Yanyu Zhang, Yuhui Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Renji Hosp Dept Spine Surg Sch Med Shanghai 200127 Peoples R China

Surface electromyographic hand gesture recognition has gained significant attention in recent years, especially within the field of human-computer interfaces. However, cross-subject tasks remain challenging due to inherent individual differences. To address this, a novel approach for hand gesture recognition is proposed that leverages a subject-generalized variational autoencoder. This approach involves an extended variational autoencoder designed to disentangle input data into three distinct feature-specific representations. The primary classifier within the variational autoencoder focuses on gesture recognition, while two auxiliary classifiers work together to extract subject-specific and gesture-specific features. The gesture-specific features capture generalized characteristics applicable across all subjects, enabling direct application to new subjects. To enhance accuracy and stability, a competitive voting strategy is implemented. The effectiveness of the proposed method was evaluated using a dataset comprising six representative gestures performed by eight subjects. Comparative analysis with baseline models shows that our approach outperforms others, demonstrating superior generalization with an average accuracy of 90.52% in cross-subject validation.

关键词： Surface electromyographic Gesture recognition Cross-subject Feature disentanglement variational autoencoder Competitive voting

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Generative autoencoder to prevent overregularization of variational autoencoder

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ETRI JOURNAL 2025年第1期47卷 80-89页

作者： Ko, YoungMin Ko, SunWoo Kim, YoungSoo Jeonju Univ Dept Artificial Intelligence Jeonju South Korea Jeonbuk Univ Artificial Intelligence Res Lab Jeonju South Korea

In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior distribution given high-dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaranteed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We compare the Frechet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets.

关键词： autoencoder data augmentation dimensionality reduction generative model variational autoencoder

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Autonomous in-situ defect detection and correction in additive-lathe 3D printing process using variational autoencoder model

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ADDITIVE MANUFACTURING 2025年 98卷

作者： Lee, Seung-Mun Park, Suk-Hee Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Additive-lathe three-dimensional (3D) printing systems, employing the material extrusion (MEX) process, deposit material layer by layer on a rotating mandrel-type bed. During this process, high layer heights can lead to defects due to unstable extrusion caused by increased extrusion rates. These defects, if not addressed, progressively worsen, deteriorating both the mechanical properties and surface finish quality of the printed parts. This study introduces a comprehensive system that integrates a machine learning model to autonomously detect and correct these defects in real-time. A variational autoencoder (VAE) model, using an unsupervised learning approach trained on defect-free images, was implemented for defect detection. The anomaly scores generated by the VAE model classify the types of defects, facilitating the application of targeted feedback controls such as filling, ironing, and speed adjustments for subsequent printing. These feedback mechanisms effectively prevent and correct defects during the printing process, enhancing the surface finish and structural quality of the printed parts. Enhancements in mechanical properties were verified through compression tests, demonstrating the capability of the developed system to improve the reliability of the additive-lathe MEX process and mitigate productivity losses associated with printing failures.

关键词： Material extrusion Deep learning variational autoencoder Process monitoring Feedback system

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A generative design method of airfoil based on conditional variational autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Wang, Xu Qian, Weiqi Zhao, Tun Chen, Hai He, Lei Sun, Haisheng Tian, Yuan China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Peoples R China Chengdu Aeronaut Polytech Chengdu 610100 Peoples R China China Aerodynam Res & Dev Ctr Low Speed Aerodynam Inst Mianyang 621000 Peoples R China

The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an efficient solution to expedite airfoil design. This study presents an innovative airfoil generative design model based on a conditional variational autoencoder (CVAE). Initially, to overcome the limitation of insufficient training data, the model leverages the variational autoencoder (VAE) to learn the spatial distribution of University of Illinois at Urbana-Champaign (UIUC) airfoils, enabling the generation of a diverse set of airfoils with similar distributions. Subsequently, two CVAE-based airfoil generation models, the airfoil freedom design model and the airfoil precision design model, are proposed, which can realize diverse airfoil design under different conditions, such as shape and aerodynamic conditions. Furthermore, two measurements of roughness and diversity are introduced to evaluate the quality of the generated airfoils. The impact of different conditions and network parameters on the model's generation performance is thoroughly analyzed. Results indicate that our proposed model achieves a 65% lower error compared to physics-guided conditional Wasserstein generative adversarial networks (PG-cWGAN) when generating airfoils that satisfy a specific lift coefficient and a 99.99% lower error compared to airfoil pressure distributions generative adversarial networks (Airfoil-Cp-GAN) when generating airfoils that satisfy specific pressure distributions. This method introduces amore creative and accurate approach for aircraft designers in the realm of airfoil design. The code used for this paper is available at https://***/liujun39/airfoilvae.

关键词： Deep learning Generative model variational autoencoder Conditional variational autoencoder Airfoil design

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Detecting and clustering structural damages using modal information and variational autoencoder with triple loss

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Huang, Shieh-Kung Li, You-Jing Lin, Yi-Xun Natl Chung Hsing Univ Dept Civil Engn 145 Xingda Rd Taichung 40227 Taiwan

Among vibration-based structural health monitoring (SHM), methods based on modal analysis are the most popular and efficient ones for assessing the inherent states of structures. The primary techniques involve tracking changes in modal parameters regularly during the monitoring period. Numerous studies have utilized both modal frequencies and mode shapes for damage detection, as mode shapes help identify damage locations, and the combination is more robust against environmental effects. Obviously, recent advances in machine learning (ML) and artificial intelligence (AI) have revolutionized SHM based on modal analysis. This study aims to develop a clustering technique using variational autoencoder (VAE) for classifying structural damages. To fully expand the entangled spaces that may arise from lower-dimensional representation, the loss function incorporates triplet loss (known as triplet VAE or tVAE) while preserving all dimensions in the encoded space. The proposed approach is thoroughly developed and evaluated using both numerical simulations and experimental investigations of multistory building structures. The effectiveness of detecting changes via VAE-based and tVAE-based modal clustering has been demonstrated under the consideration of environmental variations and uncertainties. Additionally, a procedure is suggested for the field applications and the proposed approach's capability for structural inspection during long-term monitoring is showcased using a practical scenario.

关键词： structural health monitoring damage evaluation modal parameter machine learning variational autoencoder triplet loss

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