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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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Learning Subject-Generalized Topographical EEG Embeddings Using Deep variational autoencoders and Domain-Adversarial Regularization

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SENSORS 2021年第5期21卷 1792-1792页

作者： Hagad, Juan Lorenzo Kimura, Tsukasa Fukui, Ken-ichi Numao, Masayuki Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan Osaka Univ Inst Sci & Ind Res Ibaraki Osaka 5670047 Japan

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

关键词： electroencephalography emotion modeling subject independence domain generalization variational autoencoder domain adversarial network

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Enhancing monitoring performance of data sparse nonlinear processes through information sharing among different grades using Gaussian mixture prior variational autoencoders

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 208卷 104219-104219页

作者： Lee, Yi Shan Chen, Junghui Chung Yuan Christian Univ Chung Li Dept Chem Engn Taoyuan 320 Taiwan

In the chemical industry, manufacturers produce multiple products with different grades constantly by changing the operating conditions and raw materials in the same production line to meet the customers' needs and market demands. Thus, establishing a model for monitoring such a process is inadequate because the data available in an operating region is often scarce. To deal with the hardship in monitoring the process that produces various products with multi-grades, the data sparsity problem in the target grade is addressed by simultaneously extracting and sharing common knowledge of the available grades with the target grade. In fact, multi-grade processes have similar operating conditions but they may have different ratios of ingredients. In this paper, the Gaussian mixture prior variational autoencoder (GMPVAE) is proposed to perform information sharing to enhance the target model. Complementary encoder and decoder networks represent the mapping of the source and target input data to extract the common features among the grades from the same production line. In contrast with an ordinary variational autoencoder, a Gaussian mixture model instead of a unit Gaussian distribution is set as the prior distribution of GMPVAE so that the unique characteristics of the target and source grades can be obtained. In addition, the objective function is designed on the basis of the goals to increase the reliability of the target model and to do information sharing and modeling in one step to reduce information loss during the information transferring procedure. Accordingly, the probability density estimates of latent variables and residuals rather than point estimates are then given so that distribution-based monitoring indices of the target grade can be designed and the fault detection decisions can be made opportunely. A numerical example and an industrial ammonia synthesis example are presented to show the effectiveness of the proposed method.

关键词： Data sparse Multi-grade Nonlinear process Process monitoring variational autoencoder

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Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Perez, Daniel Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain

Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system's healthy behaviour;and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.

关键词： Anomaly detection Novelty detection Deep autoencoder variational autoencoder Engineering systems

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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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Anomaly detection on household appliances based on variational autoencoders

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 32卷

作者： Castangia, Marco Sappa, Riccardo Girmay, Awet Abraha Camarda, Christian Macii, Enrico Patti, Edoardo Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Interuniv Dept Reg & Urban Studies & Planning I-10129 Turin Italy

Electrical anomalies in residential buildings represent a serious problem that can unpredictably change the power profiles of end-users, causing a sub-optimal energy distribution. In addition, electrical faults can cause unnoticed energy wastages and higher energy bills, or even severe damages for properties and people in the most critical cases. In this paper, we introduce a novel anomaly detection method for detecting electrical faults in household appliances based on the analysis of their power signatures with unsupervised deep learning techniques. For this purpose, we trained a variational autoencoder to reconstruct the power signatures of three commonly used devices: the dishwasher, the washing machine and the dryer. For each use case, we injected several randomly generated anomalies that simulate to our best realistic electrical faults in these devices. To demonstrate the effectiveness of our method, we compared the accuracy of the variational autoencoder with the classification performance of a one-class support vector machine (OC-SVM) trained with two manual features: the energy consumption and the duration of the appliance's operations. The variational autoencoder showed higher classification accuracy with respect to the OC-SVM, reporting an F1-score greater than 90% in all the use cases. Most importantly, the results demonstrate that deep anomaly detection methods outperform traditional algorithms based on handcrafted features, allowing to better characterize the set of normal cycles and produce more precise alerts for the monitored devices. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Smart grids Household appliance Anomaly detection Unsupervised learning variational autoencoder Deep learning

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VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes

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Neural Networks 2025年 189卷 107664页

作者： Shengbin Zheng Dechang Pi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accurate modeling and analysis. Recent studies have employed deep learning methods to capture and model these complexities in dynamic systems. In contrast, Koopman operator theory offers an alternative perspective, as the Koopman operator describes the linear evolution of observables in nonlinear systems within a high-dimensional space. This linearization simplifies complex nonlinear dynamics, making them easier to analyze and interpret in higher-dimensional settings. However, the Koopman operator theory does not inherently incorporate uncertainties in dynamical systems, which can hinder its performance in process monitoring. To tackle this issue, we integrate Koopman operator theory with variational autoencoders to propose a novel fault detection and isolation model called the variational Koopman Anomaly Detector (VKAD). VKAD is capable of inferring the distribution of observables from time series data of dynamical systems. By advancing the distribution through the Koopman operator over time, VKAD can capture the uncertainty in the evolution of dynamic systems. The uncertainty estimates yielded by VKAD are applicable for both fault detection and isolation in industrial processes. The effectiveness of the proposed VKAD were illustrated using the Tennessee Eastman Process (TEP) and a real satellite on-orbit telemetry dataset (SAT). The experimental results demonstrate that the Fault Detection Rate (FDR) of VKAD achieves superior performance on both the TEP and SAT datasets compared to advanced methods, while the Fault Alarm Rate (FAR) is also highly competitive.

关键词： Fault detection Fault isolation Industrial process Koopman operator theory variational autoencoder

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Developing Soft-Sensor Models Using Latent Dynamic variational autoencoders

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IFAC-PapersOnLine 2021年第3期54卷 61-66页

作者： Yi Shan Lee Sai Kit Ooi Dave Tanny Junghui Chen Department of Chemical Engineering Chung-Yuan Christian University Tao-Yuan Taiwan R.O.C.

Quality variables, which are usually measured offline, play important roles in describing process behaviors. However, online data obtained from soft sensors are significant as they provide accurate and immediate information. The reliability of online soft sensors is questionable due to changes in sensors, equipment, raw material availability, and operation conditions. In addition, chemical plants have dynamic properties and complex correlations amidst a large number of process variables. This causes most of the predictions obtained from steady-state soft sensors to be inaccurate in representing the particular chemical process. In this paper, the latent dynamic variational autoencoder is proposed to provide an estimation model and supervise soft-sensors. The input data are encoded in the latent space to remove underlying noises and disturbances in the data. Afterward, the dynamical properties are learned in the latent space through the bi-directional recurrent neural network, whose output (latent variable) is used to reconstruct back the input data. A simulation case study is conducted to show the effectiveness of the proposed method.

关键词： Dynamic Nonlinear Process Soft-Sensor Prediction Supervised variational autoencoder

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On the Use of Kullback-Leibler Divergence for Kernel Selection and Interpretation in variational autoencoders for Feature Creation

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INFORMATION 2023年第10期14卷 571页

作者： Mendonca, Fabio Mostafa, Sheikh Shanawaz Morgado-Dias, Fernando Ravelo-Garcia, Antonio G. Univ Madeira Fac Exact Sci & Engn P-9000082 Funchal Portugal Interact Technol Inst ITI LARSyS & ARDITI P-9020105 Funchal Portugal Univ Las Palmas Gran Canaria Inst Technol Dev & Innovat Commun Las Palmas Gran Canaria 35001 Spain

This study presents a novel approach for kernel selection based on Kullback-Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model's parameters. Each latent variable is sampled from the distribution associated with a single kernel of the last encoder's convolutional layer, resulting in an individual distribution for each kernel. Relevant features are selected from the sampled latent variables to perform kernel selection, which filters out uninformative features and, consequently, unnecessary kernels. Both the proposed filter method and the sequential feature selection (standard wrapper method) were examined for feature selection. Particularly, the filter method evaluates the Kullback-Leibler divergence between all kernels' distributions and hypothesizes that similar kernels can be discarded as they do not convey relevant information. This hypothesis was confirmed through the experiments performed on four standard datasets, where it was observed that the number of kernels can be reduced without meaningfully affecting the performance. This analysis was based on the accuracy of the model when the selected kernels fed a probabilistic classifier and the feature-based similarity index to appraise the quality of the reconstructed images when the variational autoencoder only uses the selected kernels. Therefore, the proposed methodology guides the reduction of the number of parameters of the model, making it suitable for developing applications for resource-constrained devices.

关键词： convolutional neural network feature selection latent variables probabilistic classifier variational autoencoder

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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

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JOURNAL OF CHEMINFORMATICS 2019年第1期11卷 70-70页

作者： Kwon, Youngchun Yoo, Jiho Choi, Youn-Suk Son, Won-Joon Lee, Dongseon Kang, Seokho Samsung Elect Co Ltd Samsung Adv Inst Technol 130 Samsung Ro Suwon South Korea Seoul Natl Univ Dept Comp Sci & Engn 1 Gwanak Ro Seoul South Korea Sungkyunkwan Univ Dept Syst Management Engn 2066 Seobu Ro Suwon South Korea

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions.

关键词： Molecular graph variational autoencoder Graph neural network Deep learning

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