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GEOENERGY SCIENCE AND ENGINEERING 2023年 229卷

作者： Yousefzadeh, Reza Ahmadi, Mohammad Amirkabir Univ Technol Dept Petr Engn Tehran Iran

Most of the geological parametrization techniques used in history matching of sub-surface formations including the deep learning-based methods could not capture the non-linear and non-Gaussian dependencies and were limited to facies realizations. This paper proposes a novel method for parametrization of permeability realizations using ConvLSTM layers to build a variational autoencoder, named ConvLSTM-VAE, for the first time in this study. Since geological layers of the reservoir models are deposited during different periods, reservoir properties have a spatial distribution along the third dimension that can be regarded as a temporal distribution. To evaluate the proposed method, its parametrization results were compared with those of 3D convolutional VAE (3DCNN-VAE), which was recently used in the literature. Quantitative and visual results showed that the proposed method was far better than the 3DCNN-VAE, where the train, validation, and test losses obtained by the ConvLSTM-VAE was 0.3663, 0.3695, and 0.3710, and by the 3DCNN-VAE was 0.3980, 0.4086, 0.4095, respectively. In addition, history matching with parametrized realizations by the ConvLSTM-VAE could preserve the geologic realism in the conditioned realizations with significantly low data mismatch errors in the historical and prediction phases;however, history matching with 3DCNN-VAE parametrization and without any parametrization could not preserve the geologic realism. Finally, this paper can guide reservoir engineers to select a proper parametrization method for highly channelized, 3D, complex reservoirs with non-linear and non-Gaussian distribution of the uncertain parameters.

关键词： Data assimilation variational autoencoder History matching Deep learning Geological parametrization

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AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2023年 107卷 107971页

作者： Wang, Mengying Li, Weimin Yu, Xiao Luo, Yin Han, Ke Wang, Can Jin, Qun Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China East China Normal Univ Sch Life Sci Shanghai Peoples R China Liaocheng Peoples Hosp Med & Hlth Ctr Liaocheng Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Australia Waseda Univ Networked Informat Syst Lab Tokyo Japan

In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes. Although employing data-driven approaches can yield favorable outcomes in deep learning, it entails a lack of interpretability. Some methods may require additional structural information or domain knowledge to support the interpretation, which may limit their applicability. This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery. In this study, the limitations of affinity prediction in terms of interpretability are tackled by proposing the concept of a protein-ligand interaction feature map. This increases the diversity and quantity of protein-ligand binding data by designing an adaptive autoencoder of target chemical properties to generate new ligands similar to known ligands and adding them to the original training set. AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction. Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model. The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.

关键词： Protein-ligand binding affinity variational autoencoder Multi-objective Multidimensional feature

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Deep multi-sphere support vector data description based on disentangled representation learning

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PATTERN RECOGNITION 2024年 156卷

作者： Xing, Hong-Jie Wu, Hui-Nan Zhang, Ping-Ping Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Univ Sch Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intellig Baoding 071002 Peoples R China Hebei Meteorol Bur Hebei Meteorol Informat Ctr Shijiazhuang 050021 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Deep support vector data description (Deep SVDD) combines deep mapping network and support vector data description (SVDD) to jointly optimize network connection weights and hypersphere volume. However, when the parameters of deep mapping network are set improperly, Deep SVDD may face the problem of hypersphere collapse, where all input data are mapped as the hypersphere center. To overcome the hypersphere collapse problem of Deep SVDD and improve the feature learning ability of deep mapping network, deep multi-sphere SVDD based on disentangled representation learning (DMSVDD-DRL) is proposed. DMSVDD-DRL consists of a variational autoencoder (VAE) and multiple hyperspheres. The feature representations obtained by VAE are disentangled into discriminative representations and generative representations that obey mixture t-distribution and Gaussian distribution, respectively. In the pre-training phase of DMSVDD-DRL, the network parameters and the hypersphere centers are initialized. In the training phase, the augmented data are added into the training set. The discriminative representations of both the input and augmented data are generated through the mapping network. Furthermore, multiple hyperspheres are constructed by the obtained discriminative representations in the feature space. Finally, the VAE loss of the input data, the reconstruction error of the augmented data, the augmentation loss between the input and augmented data, the average radius of the multiple hyperspheres, and the average distance from discriminative representations to their corresponding hypersphere centers are jointly minimized to obtain the optimal network connection weights and the multiple minimum volume hyperspheres. The effectiveness of the proposed DMSVDD-DRL is validated through the comparative and ablation experiments on the benchmark data sets. In addition, it is verified that DMSVDD-DRL is more robust against outliers in comparison with its related methods.

关键词： Deep support vector data description Disentangled representation learning variational autoencoder Hypersphere collapse Anomaly detection

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Data-driven multifidelity topology design using a deep generative model: Application to forced convection heat transfer problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 388卷 114284-114284页

作者： Yaji, Kentaro Yamasaki, Shintaro Fujita, Kikuo Osaka Univ Grad Sch Engn Dept Mech Engn 2-1 Yamadaoka Suita Osaka 5650871 Japan

Topology optimization is a powerful methodology for generating novel designs with a high degree of design freedom. In exchange for this attractive feature, topology optimization cannot generally avoid multimodality, which often impedes finding a satisfactory solution when dealing with strongly nonlinear optimization problems. In this study, we focus on constructing a framework that aims to indirectly solve such complex topology optimization problems. The framework is based on multifidelity topology design (MFTD), the basic concept of which is to divide solving an original topology optimization problem into two kinds of procedures, i.e., low-fidelity optimization and high-fidelity evaluation. We build the framework as a data-driven approach, where the design candidates given by solving the low-fidelity optimization problem are iteratively updated based on the idea of the evolutionary algorithm (EA). As a key procedure to realize a crossover-like operation in the high-dimensional design space, a variational autoencoder-one of the representative deep generative models-is utilized for generating a new dataset composed of various material distributions. Besides, we propose a mutation-like operation for generating novel material distributions based on reference ones in the dataset. By integrating these operations with an elitism-based selection procedure, we propose data-driven MFTD that enables gradient-free optimization even if tackling a complex optimization problem with a high degree of design freedom. We apply the proposed framework to forced convection heat transfer problems, where the low-fidelity optimization problem is formulated using a Darcy flow model, whereas the high-fidelity evaluation is conducted using a Navier-Stokes model. We first demonstrate that the obtained results from the proposed framework can achieve almost identical performance compared with that of the directly solved results in a well-known 2D laminar heat transfer problem. We then show that

关键词： Topology optimization Data-driven approach Multifidelity design variational autoencoder Turbulent heat transfer Minimax problem

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VASP: An autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104354-104354页

作者： von Schleinitz, Julian Graf, Michael Trutschnig, Wolfgang Schroeder, Andreas BMW AG Motorsport Munich Germany Univ Salzburg Salzburg Austria

The aim is to provide a framework for robust time series prediction in the presence of anomalies. The framework is developed based on a data set from motorsport but is not limited to this specific area. In motorsport, the usage of sensors during races is generally restricted. Estimating the outputs of these missing sensors therefore provides an advantage over the competition. Deep learning approaches such as long short-term memory (LSTM) neural networks have proven to be useful for that task, however, their accuracy decreases significantly if anomalies occur in the input signals. To overcome this problem, we propose the variational autoencoder based selective prediction (VASP) framework which combines the tasks of anomaly detection and time series prediction. VASP consists of a variational autoencoder (VAE), an anomaly detector and LSTM predictors. Depending on the anomaly detector, a subset of the inputs may be replaced by the VAE, allowing a more robust prediction. To the best of our knowledge the approach of using a VAE to only selectively replace anomalous input data before prediction has not yet been published. Our contributions are clear implementation guidelines and a comparison to other VAE-based methods and a LSTM approach as baseline. We simulate anomalies with three approaches and show that VASP outperforms other methods by having no trade-off between accuracy and robustness. VASP is as accurate as the baseline for regular data, but for anomalous inputs the error is reduced by 13% to 33% on average and up to 70% in special cases.

关键词： variational autoencoder Anomaly detection Time series prediction Motorsport Deep learning LSTM

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A survey on neural topic models: methods, applications, and challenges

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 18-18页

作者： Wu, Xiaobao Nguyen, Thong Luu, Anh Tuan Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Natl Univ Singapore Sch Comp 21 Lower Kent Ridge Rd Singapore 119077 Singapore

Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field-neural topic models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and cross-lingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research.

关键词： Neural topic model Cross-lingual topic modeling Dynamic topic modeling Short text topic modeling variational autoencoder

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PCGen: A Fully Parallelizable Point Cloud Generative Model

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SENSORS 2024年第5期24卷 1414页

作者： Vercheval, Nicolas Royen, Remco Munteanu, Adrian Pizurica, Aleksandra Univ Ghent Fac Engn & Architecture Dept Telecommun & Informat Proc Res Grp Artificial Intelligence & Sparse Modelling B-9000 Ghent Belgium Univ Ghent Fac Engn & Architecture Dept Elect & Informat Syst Clifford Res Grp B-9000 Ghent Belgium Vrije Univ Brussel Dept Elect & Informat ETRO Fac Engn B-1050 Brussels Belgium

Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions.

关键词： point clouds autoencoder variational autoencoder vector-quantized variational autoencoder real-time computing

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Disentangling the correlated continuous and discrete generative factors of data

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PATTERN RECOGNITION 2023年 133卷

作者： Choi, Jaewoong Hwang, Geonho Kang, Myungjoo Seoul Natl Univ Dept Math Sci 1 Gwanak Ro Seoul 08826 South Korea Korea Inst Adv Study KIAS Ctr Artificial Intelligence & Nat Sci Seoul South Korea

Real-world data typically include discrete generative factors, such as category labels and the existence of objects, as well as continuous generative factors. Continuous generative factors may be dependent on or independent of discrete generative factors. For instance, an intra-class variation of a category is dependent on the discrete generative factor, whereas a common variation of all categories is not. Most previous attempts to integrate discrete generative factors into disentanglement assumed statistical independence between the continuous and discrete variables. In this paper, we propose a variational autoencoder(VAE) model capable of disentangling both continuous generative factors. To represent these generative factors, we introduce two sets of continuous latent variables: a private variable and a public variable . The private and public variables represent the intra-class variations and common variations in categories, respectively. Our proposed framework models the private variable as a Gaussian mixture and the public variable as a Gaussian. Each mode of the private variable is responsible for a class of discrete variables. Our proposed model, called Discond-VAE, DISentangles the class-dependent CONtinuous factors from the Discrete factors by introducing private variables. The experiments showed that Discond-VAE could discover private and public factors from the data. Moreover, even under the dataset with only public factors, Discond-VAE does not fail and adapts private variables to represent public factors.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： variational autoencoder Disentanglement Generative model Representation learning

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An Auto-Encoder with Genetic Algorithm for High Dimensional Data: Towards Accurate and Interpretable Outlier Detection

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ALGORITHMS 2022年第11期15卷 429-429页

作者： Li, Jiamu Zhang, Ji Bah, Mohamed Jaward Wang, Jian Zhu, Youwen Yang, Gaoming Li, Lingling Zhang, Kexin Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing 210016 Peoples R China Univ Southern Queensland Sch Math Phys & Comp Toowoomba Qld 4350 Australia Zhejiang Lab Big Data Intelligence Res Ctr Hangzhou 311121 Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 243002 Peoples R China Zhengzhou Univ Aeronaut Sch Intelligent Engn Zhengzhou 450046 Peoples R China

When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicated but essential to provide interpretations for outlier detection results in high-dimensional space as a consequence of the large number of features. To alleviate these issues, we propose a new model based on a variational autoencoder and Genetic Algorithm (VAEGA) for detecting outliers in subspaces of high-dimensional data. The proposed model employs a neural network to create a probabilistic dimensionality reduction variational autoencoder (VAE) that applies its low-dimensional hidden space to characterize the high-dimensional inputs. Then, the hidden vector is sampled randomly from the hidden space to reconstruct the data so that it closely matches the input data. The reconstruction error is then computed to determine an outlier score, and samples exceeding the threshold are tentatively identified as outliers. In the second step, a genetic algorithm (GA) is used as a basis for examining and analyzing the abnormal subspace of the outlier set obtained by the VAE layer. After encoding the outlier dataset's subspaces, the degree of anomaly for the detected subspaces is calculated using the redefined fitness function. Finally, the abnormal subspace is calculated for the detected point by selecting the subspace with the highest degree of anomaly. The clustering of abnormal subspaces helps filter outliers that are mislabeled (false positives), and the VAE layer adjusts the network weights based on the false positives. When compared to other methods using five public datasets, the VAEGA outlier detection model results are highly interpretable and outperform or have competitive performance compared to current contemporary methods.

关键词： outlier detection variational autoencoder genetic algorithm abnormal subspace

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Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2024年 12卷 1350135页

作者： Dindorf, Carlo Dully, Jonas Konradi, Juergen Wolf, Claudia Becker, Stephan Simon, Steven Huthwelker, Janine Werthmann, Frederike Kniepert, Johanna Drees, Philipp Betz, Ulrich Froehlich, Michael Univ Kaiserslautern Landau Dept Sports Sci Kaiserslautern Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Phys Therapy Prevent & Rehabil Mainz Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Dept Orthoped & Trauma Surg Mainz Germany

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture ***: Data were collected from 338 subjects through surface topography. A variational autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature ***: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data ***: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

关键词： machine learning deep learning spine variational autoencoder data augmentation statistical parametric mapping

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