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Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

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ACTA MATERIALIA 2023年第1期259卷

作者： Attari, Vahid Khatamsaz, Danial Allaire, Douglas Arroyave, Raymundo Texas A&M Univ Mat Sci & Engn Dept College Stn TX 77840 USA Texas A&M Univ Mech Engn Dept College Stn TX 77840 USA

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials' responses over a spectrum of length scales. A relatively unexplored aspect of microstructure -sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials' microstructure, as well as establishing process-structure-property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep variational autoencoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials' design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. The results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase -field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

关键词： Computational materials design Process-structure-property linkage Microstructure Generative modeling Phase-field modeling Uncertainty quantification variational autoencoder

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Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality

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CHEMICAL ENGINEERING SCIENCE 2023年 265卷

作者： Lee, Yi Shan Chen, Junghui Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

The online quality variables of soft sensors contribute greatly to obtaining immediate process information. The complex correlations between a large number of process variables inherited from the dynamic and nonlinear characteristics of chemical processes put more challenges on constructing soft-sensor models. Past developed steady-state soft sensors are not reliable for dynamic operating systems. Unequal sampling rates for the process and quality data cause missing values of quality data at some time points. This paper proposes a semi-supervised latent dynamic variational autoencoder to learn features between the process and quality data. A prediction network is constructed to generate artificial quality values for model training. Then the process and quality data are compressed into the latent space and the temporal relation is modeled in the clean latent space. The proposed method is compared with the conventional method for quality prediction in a numerical case and an industrial case. CO 2022 Elsevier Ltd. All rights reserved.

关键词： Bayes inference Dynamic process Quality prediction Semi -supervised State -space model variational autoencoder

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Enhancing offline reinforcement learning for wastewater treatment via transition filter and prioritized approximation loss

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NEUROCOMPUTING 2025年 636卷

作者： Yang, Ruyue Wang, Ding Li, Menghua Cui, Chengyu Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing Peoples R China State Grid Corp China State Grid Beijing Chaoyang Power Supply Branch Beijing Peoples R China

Wastewater treatment plays a crucial role in urban society, requiring efficient control strategies to optimize its performance. In this paper, we propose an enhanced offline reinforcement learning (RL) approach for wastewater treatment. Our algorithm improves the learning process. It uses a transition filter to sort out low- performance transitions and employs prioritized approximation loss to achieve prioritized experience replay with uniformly sampled loss. Additionally, the variational autoencoder is introduced to address the problem of distribution shift in offline RL. The proposed approach is evaluated on a nonlinear system and wastewater treatment simulation platform, demonstrating its effectiveness in achieving optimal control. The contributions of this paper include the development of an improved offline RL algorithm for wastewater treatment and the integration of transition filtering and prioritized approximation loss. Evaluation results demonstrate that the proposed algorithm achieves lower tracking error and cost.

关键词： Offline reinforcement learning Wastewater treatment variational autoencoder Adaptive dynamic programming

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A novel soft sensor modeling method based on gated stacked target-supervised VAE with variable weights

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CONTROL ENGINEERING PRACTICE 2025年 155卷

作者： Xu, Liang Xie, Li Sun, Le Cao, Yuqing Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Wuxi Adv Technol Inc Wuxi 214161 Peoples R China

The variational autoencoder (VAE) has garnered extensive attention in the field of soft sensor modeling due to its superior capabilities in probabilistic data description and feature extraction. However, a single-layer VAE is challenging to extract higher-level features in the face of strong nonlinear process data. This paper proposes a gated stacked target-supervised VAE with variable weights (W-GSTVAE) to improve the modeling prediction performance of VAE. First, a stacked VAE is employed to enhance the feature extraction capability. In the pretraining phase, to enhance the correlation between the features and the target variable, feature learning is guided by incorporating the prediction error of target values into the loss function as well as calculating the maximum information coefficient between input and target variables. Meanwhile, in the fine-tuning phase, to make full use of shallow features, gated linear units are used to integrate the output features of each layer, fully exploiting the information from all layers. Finally, the effectiveness and superiority of the proposed model is demonstrated through two real industrial cases.

关键词： variational autoencoder Soft sensor modeling Target-supervised learning Maximum information coefficient Gated linear unit

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scCAT: Single-cell Combined graph Attentional clustering for scRNA-seq analysis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 105卷

作者： Gao, Yufei Zhang, Wenbo Zhang, Yameng Shi, Yujie Shi, Lei Wang, Hailing Cheng, Guozhen Henan Prov Peoples Hosp Dept Pathol Zhengzhou 450003 Henan Peoples R China Zhengzhou Univ Peoples Hosp Zhengzhou 450003 Henan Peoples R China Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou 450001 Henan Peoples R China SongShan Lab Zhengzhou 450001 Henan Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201600 Peoples R China Informat Engn Univ Inst Informat Technol Zhengzhou 450002 Henan Peoples R China

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized gene expression research at the single-cell level, enabling the study of cellular heterogeneity and identification of rare cell populations. Deep clustering is crucial for analyzing scRNA-seq datasets by assigning cells into subpopulations. However, inherent sparsity and variability in gene expression pose challenges to clustering accuracy. To address these issues, a novel unsupervised deep clustering approach named single-cell Combined graph Attentional clustering (scCAT) is introduced. The method designs a dual-branch joint dimensionality reduction (JDR) module to learn gene expression. This strategy preserves key variance while capturing complex nonlinear relationships, effectively addressing the high-dimensionality challenges of single-cell data. Additionally, a Zero-inflated negative binomial (ZINB) distribution is integrated within the JDR to tackle significant noise, sparsity, and zero-inflation challenges. A graph attention autoencoder (GATE) then processes the graph-structured data, enhancing the integration of integrating cellular topological relationships and gene expressions. Finally, a kmeans-based self-optimization technique refines clustering while synchronizing with representation learning. Experimental evaluations on eight scRNA-seq datasets demonstrate that scCAT significantly improves clustering performance, mitigates the impact of inherent data defects. The embeddings learned from both linear and non-linear perspectives eliminate inter-component interactions, uncovering complex underlying structures and enhancing the recognition of cellular topological relationships when combined with graph neural networks.

关键词： scRNA-seq Deep clustering Graph attention autoencoder variational autoencoder Zero-inflated negative binomial model

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Adaptive deep learning framework for robust unsupervised underwater image enhancement

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Saleh, Alzayat Sheaves, Marcus Jerry, Dean Azghadi, Mostafa Rahimi James Cook Univ Coll Sci & Engn 1 James Cook Dr Townsville Qld 4814 Australia James Cook Univ ARC Hub Supercharging Trop Aquaculture Genet Solut Townsville Qld Australia

One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data. Underwater images are often difficult to capture and typically suffer from distortion, colour loss, and reduced contrast, complicating the training of supervised deep learning models on large and diverse datasets. This limitation can adversely affect the performance of the model. In this paper, we propose an alternative approach to supervised underwater image enhancement. Specifically, we introduce a novel framework called Uncertainty Distribution Network (UDnet), which adapts to uncertainty distribution during its unsupervised reference map (label) generation to produce enhanced output images. UDnet enhances underwater images by adjusting contrast, saturation, and gamma correction. It incorporates a statistically guided multicolour space stretch module (SGMCSS) to generate a reference map, which is utilized by a U-Net-like conditional variational autoencoder module (cVAE) for feature extraction. These features are then processed by a Probabilistic Adaptive Instance Normalization (PAdaIN) block that encodes the feature uncertainties for the final image enhancement. The SGMCSS module ensures visual consistency with the input image and eliminates the need for manual human annotation. Consequently, UDnet can learn effectively with limited data and achieve state-of-the-art results. We evaluated UDnet on eight publicly available datasets, and the results demonstrate that it achieves competitive performance compared to other state-of-the-art methods in both quantitative and qualitative metrics. Our code is publicly available at https://***/alzayats/UDnet.

关键词： Computer vision Convolutional neural networks Underwater image enhancement variational autoencoder Machine learning Deep learning

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Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning

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OCEAN ENGINEERING 2025年 330卷

作者： Tamuly, Pranjal Sharma, Smriti Nava, Vincenzo Basque Ctr Appl Math Alameda Mazarredo 14 Bilbao 48009 Spain Politecn Torino Corso Duca Abruzzi 24 Turin Italy

The dynamics and stability of the semi-submersible offshore platforms are significantly impacted by the degradation of the mooring system. Identifying structural integrity issues in mooring systems through a data-driven approach is challenging due to the infrequency of damage events and the difficulties in recording them. To address these challenges, this study proposes the Time-Series variational Semi-Supervised Learning (TSVSSL) framework, which effectively bridges the gap between supervised and unsupervised learning by leveraging unlabelled data for damage detection. The proposed framework features a distinctive training procedure in which the encoder-decoder and classifier components are trained concurrently. This process produces a well-clustered latent representation that enhances damage detection and supports class-specific artificial data generation. A numerical study using simulated responses of a 5 MW semi-submersible FOWT under varying metocean conditions demonstrated that the proposed framework outperformed existing deep learning methods in damage detection, achieving superior accuracy, precision, recall, and F1 score. Further, a rejection sampling technique is also introduced to effectively generates artificial data that closely aligns with actual time series displacement response. The novelty of the proposed framework lies in its dual focus on damage detection and artificial data generation marking a significant advancement in the data-driven assessment of mooring systems.

关键词： Offshore structures Damage diagnosis Floating wind turbines Mooring systems Semi-supervised learning variational autoencoder

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Piecewise convolutional neural network relation extraction with self-attention mechanism

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PATTERN RECOGNITION 2025年 159卷

作者： Zhang, Bo Xu, Li Liu, Ke-Hao Yang, Ru Li, Mao-Zhen Guo, Xiao-Yang Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China Shanghai Normal Univ Inst Artificial Intelligence Educ Shanghai 200234 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH England Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 200234 Peoples R China Shanghai Newtouch Software Co Ltd Shanghai 200127 Peoples R China

The task of relation extraction in natural language processing is to identify the relation between two specified entities in a sentence. However, the existing model methods do not fully utilize the word feature information and pay little attention to the influence degree of the relative relation extraction results of each word. In order to address the aforementioned issues, we propose a relation extraction method based on self-attention mechanism (SPCNN-VAE) to solve the above problems. First, we use a multi-head self-attention mechanism to process word vectors and generate sentence feature vector representations, which can be used to extract semantic dependencies between words in sentences. Then, we introduce the word position to combine the sentence feature representation with the position feature representation of words to form the input representation of piecewise convolutional neural network (PCNN). Furthermore, to identify the word feature information that is most useful for relation extraction, an attention-based pooling operation is employed to capture key convolutional features and classify the feature vectors. Finally, regularization is performed by a variational autoencoder (VAE) to enhance the encoding ability of model word information features. The performance analysis is performed on SemEval 2010 task 8, and the experimental results show that the proposed relation extraction model is effective and outperforms some competitive baselines.

关键词： Relation extraction Multi-head attention PCNN variational autoencoder

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Integrating physics of the problem into data-driven methods to enhance elastic full-waveform inversion with uncertainty quantification

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PHYSICA D-NONLINEAR PHENOMENA 2025年 472卷

作者： Negahdari, Vahid Moghadasi, Seyed Reza Razvan, Mohammad Reza Sharif Univ Technol Dept Math Sci Tehran 111559415 Iran

Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical properties. Nevertheless, the strong nonlinearity of FWI can trap the optimization in local minima. This issue arises due to factors such as improper initial values, the absence of low frequencies in the measurements, noise, and other related considerations. To address this challenge and with the advent of advanced machine-learning techniques, data-driven methods, such as deep learning, have attracted significantly increasing attention in the geophysical community. Furthermore, the elastic wave equation should be included in FWI to represent elastic effects accurately. The intersection of data-driven techniques and elastic scattering theories presents opportunities and challenges. In this paper, by using the knowledge of elastic scattering (physics of the problem) and integrating it with machine learning techniques, we propose methods for the solution of time- harmonic FWI to enhance accuracy compared to pure data-driven and physics-based approaches. Moreover, to address uncertainty quantification, by modifying the structure of the variational autoencoder, we introduce a probabilistic deep learning method based on the physics of the problem that enables us to explore the uncertainties of the solution. According to the limited availability of datasets in this field and to assess the performance and accuracy of the proposed methods, we create a comprehensive dataset close to reality and conduct a comparative analysis of the presented approaches to it.

关键词： Full waveform inversion Inverse elastic scattering Data-driven technique Deep learning variational autoencoder Seismic imaging Uncertainty quantification

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Signal generation for bolt loosening detection with unbalanced datasets based on the CBAM-VAE

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MEASUREMENT 2025年 240卷

作者： You, Zengying Wang, Xian Xu, Jiawen Wang, Hui Yan, Ruqiang Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Jiangsu Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China Jiaotong Univ Int Machinery Ctr Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Bolt looseness has adverse influences on the stability and safety of engineering structures. Neural network algorithms can effectively monitor health conditions using impedance signals. However, impedance data of engineering structures in damaged conditions is challenging to obtain. The data would also exhibit an imbalanced distribution, yielding deterioration of the accuracy of health monitoring. In this study, we propose a data augmentation method based on a variational autoencoder model with a convolutional block attention module. This method addressed the issue of imbalanced data by generating new data. A Transformer model was adopted for training and fault classification. Without employing data augmentation methods, the max accuracy is 85.71%. However, experimental results demonstrate the remarkable effectiveness of this approach in enhancing and classifying imbalanced datasets, with an average accuracy of 89.35% and the highest accuracy of 94.81% after enhancement. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

关键词： Bolt looseness variational autoencoder Convolutional Block Attention Module Transformer Data Augmentation

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