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A survey of machine learning techniques in structural and multidisciplinary optimization

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第9期65卷 266-266页

作者： Ramu, Palaniappan Thananjayan, Pugazhenthi Acar, Erdem Bayrak, Gamze Park, Jeong Woo Lee, Ikjin Indian Inst Technol Madras Dept Engn Design Adv Design Optimizat & Probabilist Tech Lab Chennai 600036 Tamil Nadu India TOBB Univ Econ & Technol Dept Mech Engn TR-06560 Ankara Turkey Korea Adv Inst Sci & Technol Mech Engn Dept 291 Daehak Ro Daejeon 34141 South Korea

Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary optimization field. First, we discuss the challenges associated with conventional optimization and how Machine learning can address them. Then, we review the literature in the context of how ML can accelerate design synthesis and optimization. Some real-life engineering applications in structural design, material design, fluid mechanics, aerodynamics, heat transfer, and multidisciplinary design are summarized, and a brief list of widely used open-source codes as well as commercial packages are provided. Finally, the survey culminates with some concluding remarks and future research suggestions. For the sake of completeness, categories of ML problems, algorithms, and paradigms are presented in the Appendix.

关键词： Classification Clustering Deep learning Design diversity Dimension reduction Generative design Machine learning Neural network Optimization Reinforcement learning Regression Supervised unsupervised learning Uncertainty variational autoencoder

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Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 134卷 102435-102435页

作者： Halder, Sourav Yamasaki, Jun Acharya, Shashank Kou, Wenjun Elisha, Guy Carlson, Dustin A. Kahrilas, Peter J. Pandolfino, John E. Patankar, Neelesh A. Northwestern Univ Mormick Sch Engn Theoret & Appl Mech Program Evanston IL USA Northwestern Univ McCormick Sch Engn Dept Mech Engn Evanston IL USA Northwestern Univ Feinberg Sch Med Dept Med Div Gastroenterol & Hepatol Chicago IL USA Northwestern Univ Dept Mech Engn 2145 Sheridan RdRoom Tech L490 Evanston IL 60208 USA

Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map mechanical behavior of the esophageal wall in terms of mechanics-based parameters corresponding to altered bolus transit and increased intrabolus pressure. We present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of various esophageal disorders (motility disorders, eosinophilic esophagitis, reflux disease, scleroderma esophagus) and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called the functional lumen imaging probe (FLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal wall. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder that generated a latent space and a side network that predicted mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and formed clusters corre-sponding to specific esophageal disorders. The VDL not only distinguishes among disorders but also displayed disease progression over time. Finally, we demonstrated the clinical applicability of this framework for esti-mating the effectiveness of a treatment and tracking patients' condition after a treatment.

关键词： Achalasia FLIP Convolutional neural network Computational fluid dynamics Dysphagia variational autoencoder

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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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Generation of geometric interpolations of building types with deep variational autoencoders

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DESIGN SCIENCE 2020年 6卷

作者： de Miguel Rodriguez, Jaime Villafane, Maria Eugenia Piskorec, Luka Sancho Caparrini, Fernando Univ Seville Seville Spain Imperial Coll London London England Aalto Univ Espoo Finland

This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a 'connectivity map' that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through 'parametric augmentation', a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

关键词： artificial intelligence artificial neural networks computer-aided architectural design computer-aided design deep generative models deep learning deep neural networks form-finding generative design procedural design structural design variational autoencoder

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Detecting Respiratory Pathologies Using Convolutional Neural Networks and variational autoencoders for Unbalancing Data

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SENSORS 2020年第4期20卷 1214-1214页

作者： Teresa Garcia-Ordas, Maria Alberto Benitez-Andrades, Jose Garcia-Rodriguez, Isaias Benavides, Carmen Alaiz-Moreton, Hector Univ Leon Escuela Ingn Ind & Informat SECOMUCI Res Grp Campus Vegazana S-N E-24071 Leon Spain Univ Leon Dept Elect Syst & Automat Engn SALBIS Res Grp Campus Vegazana S-N E-24071 Leon Spain

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a variational Convolutional autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

关键词： CNN variational autoencoder respiratory lungs pathologies

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Generating Musical Sequences with a Given Emotion

Generating Musical Sequences with a Given Emotion

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Grekow, Jacek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

ISBN: (纸本)9781665442077

This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, which correspond to the four quarters of Russell's model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated through comparison with the training set.

关键词： Generative models music generation music emotion variational autoencoder

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DVAE-SR: Denoiser variational Auto-Encoder and Super-Resolution to Counter Adversarial Attacks 13

DVAE-SR: Denoiser Variational Auto-Encoder and Super-Resolut...

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13th International Conference on Machine Vision

作者： Dardour, Omar Zaied, Mourad Radeva, Petia Univ Gabes Zrig Eddakhlania Tunisia Univ Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781510640412

Recently, adversarial examples become one of the most dangerous risks in deep learning, which affects applications of real world such as robotics, cyber-security and computer vision. In image classification, adversarial attacks showed the ability to fool classifiers with small imperceptible perturbations added to the input. In this paper, we present an efficient defense mechanism, we call DVAE-SR that combine variational autoencoder and super-resolution to eliminate adversarial perturbation from image input before feeding it to the CNN classifier. The DVAE-SR can successfully defend against both white-box and black-box attacks without retraining CNN classifier and it recovers better accuracy than Defense-GAN and Defense-VAE..

关键词： Adversarial attacks Adversarial defense variational autoencoder Super-resolution Deep learning Image classification Computer vision

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Learning Traffic as Videos: A Spatio-Temporal VAE Approach for Traffic Data Imputation 30th

Learning Traffic as Videos: A Spatio-Temporal VAE Approach f...

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30th International Conference on Artificial Neural Networks (ICANN)

作者： Chen, Jiayuan Zhang, Shuo Chen, Xiaofei Jiang, Qiao Huang, Hejiao Gu, Chonglin Harbin Inst Technol Shenzhen Dept Comp Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9783030863838;9783030863821

In the real world, data missing is inevitable in traffic data collection due to detector failures or signal interference. However, missing traffic data imputation is non-trivial since traffic data usually contains both temporal and spatial characteristics with inherent complex relations. In each time interval, the traffic measurements collected in all spatial regions can be regarded as an image with more or fewer channels. Therefore, the traffic raster data over time can be learned as videos. In this paper, we propose a novel unsupervised generative neural network for traffic raster data imputation called STVAE, which works well robustly even under different missing rates. The core idea of our model is to discover more complex spatio-temporal representations inside the traffic data under the architecture of variational autoencoder (VAE) with Sylvester normalizing flows (SNFs). After transforming the traffic raster data into multi-channel videos, a Detection-and-Calibration Block (DCB), which extends 3D gated convolution and multi-attention mechanism, is proposed to sense, extract and calibrate more flexible and accurate spatio-temporal dependencies of the original data. The experiments are employed on three real-world traffic flow datasets and demonstrate that our network STVAE achieves the lowest imputation errors and outperforms state-of-the-art traffic data imputation models.

关键词： Traffic data imputation Unsupervised learning variational autoencoder Normalizing flows Attention mechanism 3D gated convolution

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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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Multicategory choice modeling with sparse and high dimensional data: A Bayesian deep learning approach

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DECISION SUPPORT SYSTEMS 2022年 157卷 113766-113766页

作者： Xia, Feihong Chatterjee, Rabikar Univ Rhode Isl Coll Business 233 Ballentine Hall7 Lippitt Rd Kingston RI 02881 USA Univ Pittsburgh Joseph M Katz Grad Sch Business 276C Mervis Hall Pittsburgh PA 15260 USA

The availability of sparse and high dimensional consumer shopping data poses a challenge for researchers for accurate and efficient analysis. While deep learning models can handle such data, most of the results from these models are uninterpretable. This greatly limits their value in applications aimed at better understanding multicategory shopping behavior and assisting managerial decision-making. Thus, a new approach is needed to analyze high dimensional consumer shopping data in an efficient and interpretable manner, with the potential to greatly strengthen decision support systems for managers especially at large consumer packaged goods firms and giant retailers. We propose a Bayesian deep learning approach based on variational autoencoder (VAE) designed to efficiently capture often complex and multifaceted interrelationships across items in the shopping baskets, in the form of substitute and complementary cross effects (which are observable and controllable) and coincidences (which are not). The benefits of the proposed approach are empirically demonstrated by applying our model to a high dimensional supermarket shopping dataset covering very large numbers of products at the stock-keeping unit (SKU) level, households, and shopping trips. We discuss implications for managerial decision-making and identify promising research directions.

关键词： Consumer shopping basket Consumer multicategory choice modeling Sparsity High dimensional data variational autoencoder Bayesian deep learning

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