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Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第734期147卷 139-149页

作者： Grooms, Ian Univ Colorado Dept Appl Math Boulder CO 80309 USA

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs;a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-'96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.

关键词： analogs Ensemble Optimal Interpolation machine learning variational autoencoder

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Learning Physically Meaningful Representations of Energy Systems with variational autoencoders 27

Learning Physically Meaningful Representations of Energy Sys...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Multaheb, Samim Bauer, Fabian Bretschneider, Peter Niggemann, Oliver Helmut Schmidt Univ Inst Automat Technol Hamburg Germany Ilmenau Univ Technol Inst Elect Power & Control Engn Ilmenau Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.

关键词： Representation Learning Neural Networks variational autoencoder Explainable Artificial Intelligence Informed Machine Learning

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Rolling Bearing Fault Diagnosis Based on Multi-Modal variational autoencoders 3

Rolling Bearing Fault Diagnosis Based on Multi-Modal Variati...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xiong, Manjun Wu, Yifan Li, Chuan Yang, Zhe School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China Chongqing Technology and Business University Research Center of System Health Maintenance Chongqing400067 China School of Mechanical Engineering Dongguan University of Technology and Business University Dongguan523808 China

ISBN: (纸本)9781665492812

With the development of Industry 4.0, more and more attention has been paid to system intelligent maintenance by various industries, among which rolling bearing is an indispensable and most important component. Existing methods have such limitations as the need for prior knowledge and manual feature extraction. For this reason, a multi-modal variational autoencoder (MMVAE) is proposed to extract useful features from multiple modalities. Firstly, the fault characteristics of multiple modalities are extracted separately by different variational autoencoders containing complementary information. Secondly, a collaborative training method is proposed to maximize mutual consistency. Specifically, feature extraction and clustering for all modalities are employed for collaborative learning. Fault diagnosis experiments on a benchmark rolling bearing dataset were carried out. Compared with other methods, MMVAE showed remarkable results, with an accuracy of 99.13%. © 2022 IEEE.

关键词： fault diagnosis intelligent computing multi-modality rolling bearing variational autoencoder

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Unsupervised deep learning approach for structural anomaly detection using probabilistic features

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年第1期24卷 3-33页

作者： Wan, Hua-Ping Zhu, Yi-Kai Luo, Yaozhi Todd, Michael D. Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Peoples R China Univ Calif San Diego Dept Struct Engn La Jolla CA USA

Civil structures may deteriorate during their service life due to degradation or damage imposed by natural hazards such as earthquakes, wind, and impact. Structural performance anomaly detection is essential to provide an early warning of structural degradation limit states in order to prevent potential catastrophic failure. Data-driven machine learning approaches have been widely used for this, due to their capability in capturing features sensitive to damage-induced anomalies from structural health monitoring (SHM) data, assuming that such data are available. Although machine learning models have been used, many are challenged by the vast operational and environmental variability that can corrupt SHM data and by (typically) strongly correlated information from different sensors in the SHM data. This paper proposes an unsupervised deep learning approach for the detection of structural anomaly based on a deep convolutional variational autoencoder (DCVAE) for feature extraction coupled with support vector data description (SVDD) for anomaly detection. The proposed DCVAE-SVDD method has several appealing strengths. First, the variational latent encoding is used to capture the features of monitoring data through a probability distribution. The integration of the Kullback-Leibler divergence in the loss function provides accurate estimation of the probability distributions. Second, the DCVAE designed with convolutional and deconvolutional operations utilizes the correlation among multisensor data to avoid loss of correlation features and achieve better performance in feature extraction. Third, the SVDD is utilized to create a minimum-volume hypersphere that contains the anomaly-sensitive statistical features of the state. The hypersphere accurately separates anomaly-sensitive statistical features of reference states of structure from the anomalous ones. A computational frame model and a laboratory grandstand model are used to evaluate the performance of the proposed meth

关键词： Structural anomaly detection structural health monitoring variational autoencoder support vector data description unsupervised deep learning

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Physically reliable 3D styled shape generation via structure-aware topology optimization in unified latent space

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COMPUTER-AIDED DESIGN 2025年 183卷

作者： Ijaz, Haroon Wang, Xuwei Chen, Wei Lin, Hai Li, Ming Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Peoples R China

We propose a novel approach to structure-aware topology optimization (SATO) to generate physically plausible multi-component structures with diverse stylistic variations. Traditional TO methods often operate within a discrete voxel-defined design space, overlooking the underlying structure-aware, which limits their ability to accommodate stylistic design preferences. Our approach leverages variational autoencoders (VAEs) to encode both geometries and corresponding structures into a unified latent space, capturing part arrangement features. The design target is carefully formulated as a topology optimization problem taking the VAE code as design variables under physical constraints, and solved numerically via analyzing the associated sensitivity with respect to the VAE variables. Our numerical examples demonstrate the ability to generate lightweight structures that balance geometric plausibility and structural performance with much enhanced stiffness that outperforms existing generative techniques. The method also enables the generation of diverse and reliable designs, maintaining structural integrity throughout, via a direct smooth interpolation between the optimized designs. The findings highlight the potential of our approach to bridge the gap between generative design and physics-based optimization by incorporating deep learning techniques.

关键词： Generative design 3D shape creation Topology optimization variational autoencoder Finite element analysis Global convergent MMA

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Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual variational autoencoders

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SMALL 2023年第25期19卷 2205893-2205893页

作者： Yaman, Muammer Y. Kalinin, Sergei V. Guye, Kathryn N. Ginger, David S. Ziatdinov, Maxim Univ Washington Dept Chem Seattle WA 98195 USA Univ Tennessee Dept Mat Sci & Engn Knoxville TN 37996 USA Pacific Northwest Natl Lab Phys Sci Div Phys & Computat Sci Directorate Richland WA 99354 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

关键词： darkfield scattering spectra machine learning plasmonic gold particles scanning electron microscopy structure-property prediction variational autoencoder

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mcVAE: disentangling by mean constraint

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VISUAL COMPUTER 2024年第2期40卷 1229-1243页

作者： Hu, Ming-fei Liu, Ze-yu Liu, Jian-wei China Univ Petr Dept Automat Coll Informat Sci & Engn 260 Mailbox Beijing 102249 Peoples R China

Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, beta-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the beta-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms beta-VAE on several benchmark datasets.

关键词： variational autoencoder Disentanglement Representation learning

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Rethinking Robust Multivariate Time Series Anomaly Detection: A Hierarchical Spatio-Temporal variational Perspective

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 9136-9149页

作者： Zhang, Xiao Xu, Shuqing Chen, Huashan Chen, Zekai Zhuang, Fuzhen Xiong, Hui Yu, Dongxiao Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Amazon Seattle WA 98109 USA Beihang Univ Inst Artificial Intelligence Sch Comp Sci SKLSDE Beijing 100191 Peoples R China Hong Kong Univ Sci & Technol Thrust Artificial Intelligence Guangzhou 511458 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

The robust multivariate time series anomaly detection can facilitate intelligent decisions and timely maintenance in various kinds of monitor systems. However, the robustness is highly restricted by the stochasticity in multivariate time series, which is summarized as temporal stochasticity and spatial stochasticity specifically. In this paper, we explicitly model the temporal stochasticity variables and the latent graph relationship variables into a unified graphical framework, which can achieve better robustness to dynamicity from both the spatial and temporal perspective. First, within the spatial encoder, every connection exists or not is modeled as a binary stochastic variable, and the graph structure can be learnt automatically. Then, the temporal encoder would embed the highly structured time series into latent stochastic variables to capture both complex temporal dependencies and neighbors information. Moreover, we design a history-future combined anomaly score mechanism with both reconstruction decoder and forecasting decoder to improve the anomaly detection performance. By weighting the historical anomaly factor, the future anomaly factor, and the prediction error of current timestamp, the anomaly detection at current timestamp could be more sensitive to anomaly detection. Finally, extensive experiments on three publicly available anomaly detection datasets demonstrate our proposed method can achieve the best performance in terms of recall and F1 compared with state-of-the-arts baselines.

关键词： Time series analysis Anomaly detection Decoding Stochastic processes Predictive models Robustness Data models variational autoencoder multivariate time series self-attention

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Parametric model order reduction by machine learning for fluid-structure interaction analysis

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ENGINEERING WITH COMPUTERS 2024年第1期40卷 45-60页

作者： Lee, SiHun Jang, Kijoo Lee, Sangmin Cho, Haeseong Shin, SangJoon Seoul Natl Univ Dept Aerosp Engn Seoul 08226 South Korea Jeonbuk Natl Univ Dept Aerosp Engn Jeonju 54896 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08226 South Korea

An improved nonintrusive parametric model order reduction (pMOR) approach is proposed for the flow field interpolation regarding fluid-structure interaction (FSI) objects. Flow field computation using computational fluid dynamics (CFD) requires excessive computational time and memory. Nonintrusive and data-driven MOR schemes have been proposed to overcome such limitations. The present methodology is implemented by both proper orthogonal decomposition (POD) and a modified Nouveau variational autoencoder (mNVAE). POD attempts to reduce the number of degrees of freedom (DOFs) on the precomputed series of the full-order model parametric result. The reduced DOF yields parametrically independent reduced bases and dependent coefficients. Then, mNVAE is employed for the interpolation of POD coefficients, which will be combined with POD modes for parametrically interpolated flow field generation. The present approach is assessed on the benchmark problem of a two-dimensional plunging airfoil and the highly nonlinear FSI phenomenon of the limit cycle oscillation. The comparison was executed against other POD-based generative neural network approaches. The proposed methodology demonstrates applicability on highly nonlinear FSI objects with improved accuracy and efficiency.

关键词： Nonintrusive parametric reduced-order modeling Machine learning Fluid-structure interaction Proper orthogonal decomposition variational autoencoder

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Low frequency residential load monitoring via feature fusion and deep learning

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Ji, Tianyao Chen, Jiawei Zhang, Luliang Lai, Hongfeng Wang, Jian Wu, Qinghua South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

Non-intrusive load monitoring (NILM) is a technique used to disaggregate the total power signal into individual appliance power signals, which plays an important role in smart grid. Recently, deep learning is widely used to deal with the NILM problem. However, current deep learning models are purely data-driven, which do not consider physical mechanisms, making them less effective in extracting useful features. To address these issues, a new approach for feature extraction based on variational mode decomposition (VMD) and a new deep learning model based on variational autoencoder (VAE) are developed in this paper. The proposed feature extraction approach extracts the pulse feature and concatenates it with the original power data to form multiple features, i.e., which achieves feature fusion to improve the performance of deep learning models better than with a single feature. In addition, a feedback variational mode decomposition (FVMD) is proposed to improve the decomposition performance of the original VMD. The channel attention mechanism is introduced to VAE to improve the performance of the model. To verify the accuracy and robustness of the proposed scheme in NILM, it is compared with the state-of-the-art models on the UK-DALE dataset, and the results show that the proposed feature extraction approach can greatly improve the performance of deep learning models and the proposed new deep learning model outperforms some state-of-the-art models in the realm of NILM.

关键词： Non-intrusive load monitoring Feature fusion Feedback variational mode decomposition variational autoencoder Channel attention mechanism

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