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Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil

ENGINEERING WITH COMPUTERS 2023年第3期39卷 2239-2255页

作者： Yang, Sunwoong Lee, Sanga Yee, Kwanjung Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Korea Inst Ind Technol Incheon 21999 South Korea

The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations that prevent it from achieving full efficiency. First, the iterative procedure should be repeated whenever the specified target distribution changes. Target distribution optimization can be performed to clarify the ambiguity in specifying this distribution, but several additional problems arise in this process such as loss of the representation capacity due to parameterization of the distribution, excessive constraints for a realistic distribution, inaccuracy of quantities of interest due to theoretical/empirical predictions, and the impossibility of explicitly imposing geometric constraints. To deal with these issues, a novel inverse design optimization framework with a two-step deep learning approach is proposed. A variational autoencoder and multi-layer perceptron are used to generate a realistic target distribution and predict the quantities of interest and shape parameters from the generated distribution, respectively. Then, target distribution optimization is performed as the inverse design optimization. The proposed framework applies active learning and transfer learning techniques to improve accuracy and efficiency. Finally, the framework is validated through aerodynamic shape optimizations of the wind turbine airfoil. Their results show that this framework is accurate, efficient, and flexible to be applied to other inverse design engineering applications.

关键词： Inverse design optimization Multi-layer perceptron variational autoencoder Wind turbine airfoil

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A deep learning factor analysis model based on importance-weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players

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STATISTICAL ANALYSIS AND DATA MINING-AN ASA DATA SCIENCE JOURNAL 2023年第5期16卷 474-487页

作者： Kilian, Pascal Leyhr, Daniel Urban, Christopher J. Hoener, Oliver Kelava, Augustin Univ Tubingen Methods Ctr Tubingen Germany Univ Tubingen Inst Sports Sci Methods Ctr Tubingen Germany Univ North Carolina Chapel Hill Dept Psychol & Neurosci Chapel Hill NC USA Univ Tubingen Inst Sports Sci Tubingen Germany

Exploratory factor analysis is a widely used framework in the social and behavioral sciences. Since measurement errors are always present in human behavior data, latent factors, generating the observed data, are important to identify. While most factor analysis methods rely on linear relationships in the data-generating process, deep learning models can provide more flexible modeling approaches. However, two problems need to be addressed. First, for interpretation, scaling assumptions are required, which can be (at least) cumbersome for deep generative models. Second, deep generative models are typically not identifiable, which is required in order to identify the underlying latent constructs. We developed a model that uses a variational autoencoder as an estimator for a complex factor analysis model based on importance-weighted variational inference. In order to receive interpretable results and an identified model, we use a linear factor model with identification constraints in the measurement model. To maintain the flexibility of the model, we use normalizing flow latent priors. Within the evaluation of performance measures in a talent development program in soccer, we found more clarity in the separation of the identified underlying latent dimensions with our models compared to traditional PCA analyses.

关键词： complex factor analysis deep latent variable models latent variable identification soccer talent research variational autoencoder

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Masked autoencoder Transformer for Missing Data Imputation of PISA 25th

Masked Autoencoder Transformer for Missing Data Imputation o...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Freire, Guilherme Mendonca Curi, Mariana Univ Sao Paulo Inst Ciencias Matemat & Comp Ave Trab Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil

ISBN: (纸本)9783031643149;9783031643156

This study introduces a scale-down transformer model to address the challenge of missing responses in educational assessments for psychometric evaluation. Traditional estimation methods for Item Response Theory (IRT) models are frequently computationally inefficient in generating estimates for a higher number of dimensions. The challenge becomes more pronounced when dealing with missing responses. We propose a Masked autoencoder Transformer model for discrete input (DiTMAE) to impute missing answers for OECD-PISA response data. The model learns context information from the unmasked parts and reconstructs it using a decoder. For evaluation purposes, we estimate item and person parameters from two different approaches, (i) an adapted variational autoencoder that incorporates the Item Response Theory (IRT) method;(ii) the traditional statistical tool, Joint Maximum Likelihood (JML), that can produce estimates in occurrence of missing values.

关键词： Item Response Theory Missing Data Neural Networks Transformer Model variational autoencoder

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Maximizing mutual information inside intra- and inter-modality for audio-visual event retrieval

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2023年第1期12卷 10页

作者： Li, Ruochen Li, Nannan Wang, Wenmin Macau Univ Sci & Technol Sch Engn & Comp Sci Ave Wai Long Taipa 999078 Macau Peoples R China

The human brain can process sound and visual information in overlapping areas of the cerebral cortex, which means that audio and visual information are deeply correlated with each other when we explore the world. To simulate this function of the human brain, audio-visual event retrieval (AVER) has been proposed. AVER is about using data from one modality (e.g., audio data) to query data from another. In this work, we aim to improve the performance of audio-visual event retrieval. To achieve this goal, first, we propose a novel network, InfoIIM, which enhance the accuracy of intra-model feature representation and inter-model feature alignment. The backbone of this network is a parallel connection of two VAE models with two different encoders and a shared decoder. Secondly, to enable the VAE to learn better feature representations and to improve intra-modal retrieval performance, we have used InfoMax-VAE instead of the vanilla VAE model. Additionally, we study the influence of modality-shared features on the effectiveness of audio-visual event retrieval. To verify the effectiveness of our proposed method, we validate our model on the AVE dataset, and the results show that our model outperforms several existing algorithms in most of the metrics. Finally, we present our future research directions, hoping to inspire relevant researchers.

关键词： Audio-visual retrieval variational autoencoder Mutual information InfoMax-VAE

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Using deep learning to generate design spaces for architecture

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2023年第2期21卷 337-357页

作者： Sebestyen, Adam Hirschberg, Urs Rasoulzadeh, Shervin Graz Univ Technol Inst Architecture & Media Graz Austria Vienna Univ Technol Inst Bldg & Ind Construct Vienna Austria Graz Univ Technol Inst Architecture & Media Kronesgasse 5-3 A-8010 Graz Austria

We present an early prototype of a design system that uses Deep Learning methodology-Conditional variational autoencoders (CVAE)-to arrive at custom design spaces that can be interactively explored using semantic labels. Our work is closely tied to principles of parametric design. We use parametric models to create the dataset needed to train the neural network, thus tackling the problem of lacking 3D datasets needed for deep learning. We propose that the CVAE functions as a parametric tool in itself: The solution space is larger and more diverse than the combined solution spaces of all parametric models used for training. We showcase multiple methods on how this solution space can be navigated and explored, supporting explorations such as object morphing, object addition, and rudimentary 3D style transfer. As a test case, we implemented some examples of the geometric taxonomy of "Operative Design" by Di Mari and Yoo.

关键词： deep learning generative methods parametric design design space 3D object generation variational autoencoder operative design artificial intelligence machine learning voxels

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A two-step framework for arbitrage-free prediction of the implied volatility surface

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QUANTITATIVE FINANCE 2023年第1期23卷 21-34页

作者： Zhang, Wenyong Li, Lingfei Zhang, Gongqiu Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

In this study, we propose a two-step framework to predict the implied volatility surface (IVS) in a manner that excludes static arbitrage. First, we select features to represent the surface and predict them. Second, we use the predicted features to construct the IVS using a deep neural network (DNN) model by incorporating constraints that can prevent static arbitrage. We consider three methods to extract features from the implied volatility data: principal component analysis, variational autoencoder, and sampling the surface. We predict these features using the long short-term memory model. Additionally, we use a long time series of implied volatility data for S&P500 index options to train our models. We find that two feature construction methods (i.e. sampling the surface and variational autoencoders combined with DNN for surface construction) are the best performers in the out-of-sample prediction. Furthermore, both of them substantially outperform a popular regression model. We also find that the DNN model for surface construction not only removes static arbitrage but also significantly reduces the prediction error compared with a standard interpolation method.

关键词： Implied volatility surface Static arbitrage Prediction Deep learning variational autoencoder

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Generating subsurface earth models using discrete representation learning and deep autoregressive network

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COMPUTATIONAL GEOSCIENCES 2023年第6期27卷 955-974页

作者： Chen, Jungang Huang, Chung-Kan Delgado, Jose F. Misra, Siddharth Texas A&M Univ Coll Engn Dept Petr Engn College Stn TX 77843 USA Conoco Phillips Houston TX USA Texas A&M Univ Coll Geosci Dept Geol & Geophys College Stn TX 77843 USA

Subsurface earth models (referred as geomodels) are crucial for characterizing complex subsurface systems. Multiple-point statistics is commonly used to generate geomodels. In this paper, a deep-learning-based generative method is developed as an alternative to traditional geomodel generation procedure. The generative method comprises two deep-learning models, namely hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and PixelSNAIL autoregressive model. Based on the principle of neural discrete representation learning, the VQ-VAE-2 learns to massively compress the geomodels to extract the low-dimensional, discrete latent representation corresponding to each geomodel. Following that, PixelSNAIL uses deep autoregressive network to learn the prior distribution of the latent codes. For the purpose of geomodel generation, PixelSNAIL samples from the newly learnt prior distribution of latent codes, and then the decoder of the VQ-VAE-2 converts the newly sampled latent code to a newly constructed geomodel. PixelSNAIL can be used for unconditional or conditional geomodel generation. In unconditional generation, the generative workflow generates an ensemble of geomodels without any constraint. On the other hand, in the conditional geomodel generation, the generative workflow generates an ensemble of geomodels similar to a user-defined source image, which ultimately facilitates the control and manipulation of the generated geomodels. To better construct the fluvial channels in the geomodels, perceptual loss is implemented in the VQ-VAE-2 model instead of the traditional 'mean squared error' loss. At a specific compression ratio, the quality of multi-attribute geomodel generation is better than that of single-attribute geomodel generation.

关键词： Vector quantization variational autoencoder Perceptual loss Autoregression Deep generative model Compression

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scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

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GENOME BIOLOGY 2023年第1期24卷 1页

作者： Zhu, Biqing Wang, Yuge Ku, Li-Ting van Dijk, David Zhang, Le Hafler, David A. Zhao, Hongyu Yale Univ Program Computat Biol & Bioinformat New Haven CT 06511 USA Yale Univ Sch Publ Hlth Dept Biostat New Haven CT 06511 USA Yale Sch Med Dept Internal Med New Haven CT 06511 USA Yale Univ Dept Comp Sci New Haven CT 06511 USA Yale Univ Sch Med Dept Neurosci New Haven CT 06511 USA Yale Univ Sch Med Dept Neurol New Haven CT 06511 USA Yale Univ Sch Med Dept Immunobiol New Haven CT 06511 USA Aligning Sci Parkinsons ASAP Collaborat Res Networ Chevy Chase MD 20815 USA

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

关键词： scRNA-seq scTCR-seq Data integration Deep learning variational autoencoder Clone expansion

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Non-linear Vibration Response Analysis of Rolling Bearing for Data Augmentation and Characterization

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2023年第5期11卷 2109-2131页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Adv Mech Vibrat Lab Roorkee 247667 Uttarakhand India

The bearing fault diagnosis is carried out using nonlinear vibration responses using a proposed framework of VAEGAN-RDCNN. The condition of imbalanced data augmentation is solved using variational auto-encoder generative adversarial network (VAEGAN). The characterization of 2D patterns (phase space trajectories) into conditions of chaos (strong and weak attractor), and quasiperiodic is implemented using residual deep convolutional neural network (RDCNN). Experimentation is carried out on a test rig for phase space reconstruction. Various metrics such as PSNR, SSIM, KLD, and histogram analysis are used to investigate the quality of generated samples. Performance assessment of the proposed method is demonstrated using VAE, and WGAN models based on metrics namely average accuracy, precision, recall, and F1-score. The comparison results obtained are 98.20%, 90.13%, and 94.56% corresponding to VAEGAN-RDCNN, VAE, and WGAN, respectively. The discriminative feature learning ability of RDCNN is also presented using t-SNE technique. Hence, the proposed data augmentation and characterization methodology works effectively and achieves superior results to conventional data synthesis-based approaches.

关键词： Pattern classification Residual learning Generative adversarial networks variational autoencoder Deep convolutional neural network

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New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

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MOLECULAR ECOLOGY RESOURCES 2023年第8期23卷 1946-1958页

作者： Lamperti, Letizia Sanchez, Theophile Moussi, Sara Si Mouillot, David Albouy, Camille Fluck, Benjamin Bruno, Morgane Valentini, Alice Pellissier, Loic Manel, Stephanie PSL Univ Univ Montpellier EPHE CEFECNRSIRD Montpellier France Swiss Fed Inst Technol Dept Environm Syst Sci Ecosyst & Landscape Evolut Zurich Switzerland Swiss Fed Inst Forest Snow & Landscape Res WSL Land Change Sci Res Unit Ecosyst & Landscape Evolut Birmensdorf Switzerland Univ Grenoble Alpes Univ Savoie MontBlanc CNRS Lab Ecol Alpine Grenoble France Univ Montpellier MARBEC CNRS IFREMERIRD Montpellier France Inst Univ France Paris France SPYGEN Le Bourget Du Lac France

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning-based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two-dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two-dimensional latent space: MOTU richness per sample, sequence a-diversity per sample, Jaccard's and sequence beta-diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t-distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.

关键词： biodiversity monitoring data visualization deep learning deep metric learning environmental DNA machine learning neural networks variational autoencoder

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