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Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials

ADVANCED MATERIALS 2024年第6期36卷 2303481-2303481页

作者： Pahlavani, Helda Tsifoutis-Kazolis, Kostas Saldivar, Mauricio C. Mody, Prerak Zhou, Jie Mirzaali, Mohammad J. Zadpoor, Amir A. Delft Univ Technol TU Delft Fac Mech Maritime & Mat Engn Dept Biomech Engn Mekelweg 2 NL-2628 CD Delft Netherlands Leiden Univ Med Ctr LUMC Div Image Proc LKEB Radiol Albinusdreef 2 NL-2333 ZA Leiden Netherlands

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem. Mechanical metamaterials are architected engineered materials whose unprecedented properties originate from their designs at the microscale. Here, deep-learning techniques are utilized to develop a modular approach titled "Deep-DRAM" that generates numerous microstructures for mechanical metamaterials with predefined elastic properties and dimensions for various applications in high-tech industries, such as soft robotics and biomedical ***

关键词： additive manufacturing deep learning numerical simulations random-network mechanical metamaterials size-agnostic variational autoencoder

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Progressive generation of 3D point clouds with hierarchical consistency

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PATTERN RECOGNITION 2023年 136卷

作者： Li, Peipei Liu, Xiyan Huang, Jizhou Xia, Deguo Yang, Jianzhong Lu, Zhen Beijing Univ Posts & Telecommun Sch Artificial Intelligence 10 Xitucheng Rd Beijing 100876 Peoples R China Baidu Inc Baidu Technol Pk Bldg 210 Xibeiwang East Rd Beijing 100193 Peoples R China

Generating 3D point cloud directly from latent prior (e.g., Gaussian distribution) plays a vital role in the representation learning and data augmentation in 3D vision tasks. Since point cloud is formed by irregu-lar points, the generation process of point cloud requires rich semantic information, yet few studies are devoted to it. In this paper, we recast this generation task as a progressive learning problem to model the two-level hierarchy of distributions and address it by proposing a novel model called hierarchical consis-tency variational autoencoder (HC-VAE). This framework introduces a hierarchical consistent mechanism (HCM) to model the shape consistency and the pointwise representation consistency in a complementary manner. Specifically, we propose a stackable encoder-decoder framework and constrain the generation quality progressively to ensure that the underlying shape and fine-grained parts can be reconstructed with high fidelity. Additionally, given the progressively generated intermediate point cloud instances, a hierarchical-positive contrastive loss is introduced to learn the point-distribution-free instance represen-tations to avoid explicitly parametrizing the distribution of points in a shape. In this way, our model suffices to generate diverse, high-resolution, and uniform point cloud instances. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in point cloud gen-eration.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： 3D Point cloud generation Point cloud analysis Generative adversarial networks variational autoencoder Hierarchical consistency

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Feature Extraction Based on Self-Supervised Learning for Remaining Useful Life Prediction

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第2期24卷 021004-021004页

作者： Yu, Zhenjun Lei, Ningbo Mo, Yu Xu, Xin Li, Xiu Huang, Biqing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Shenzhen Int Grad Sch Div Informat Sci Shenzhen 518055 Peoples R China China Nucl Power Engn Co Ltd Beijing 100840 Peoples R China

The prediction of the remaining useful life (RUL) is of great significance to ensure the safe operation of industrial equipment and to reduce the cost of regular preventive maintenance. However, the complex operating conditions and various fault modes make it difficult to extract features containing more degradation information with existing prediction methods. We propose a self-supervised learning method based on variational automatic encoder (VAE) to extract features of data's operating conditions and fault modes. Then the clustering algorithm is applied to the extracted features to divide data from different failure modes into different categories and reduce the impact of complex working conditions and fault modes on the estimation accuracy. In order to verify the effectiveness of the proposed method, we conduct experiments with different network structures on the C-MAPSS dataset, and the results verified that our method can effectively improve the feature extraction capability of the model. In addition, the experimental results further demonstrate the superiority and necessity of using hidden features for clustering rather than raw data.

关键词： fault prognosis remaining useful life prediction self-supervised learning variational autoencoder

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GRAINS: Generative Recursive autoencoders for INdoor Scenes

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ACM TRANSACTIONS ON GRAPHICS 2019年第2期38卷 12-12页

作者： Li, Manyi Patil, Akshay Gadi Xu, Kai Chaudhuri, Siddhartha Khan, Owais Shamir, Ariel Tu, Changhe Chen, Baoquan Cohen-Or, Daniel Zhang, Hao Shandong Univ Qingdao Shandong Peoples R China Simon Fraser Univ Vancouver BC Canada Natl Univ Def Technol Changsha Hunan Peoples R China Adobe Res Bangalore Karnataka India Indian Inst Technol Mumbai Maharashtra India Interdisciplinary Ctr Herzliyya Israel Peking Univ Beijing Peoples R China Tel Aviv Univ Tel Aviv Israel

We present a generative neural network that enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation is that indoor scene structures are inherently hierarchical. Hence, our network is not convolutional;it is a recursive neural network, or RvNN. Using a dataset of annotated scene hierarchies, we train a variational recursive autoencoder, or RvNN-VAE, which performs scene object grouping during its encoding phase and scene generation during decoding. Specifically, a set of encoders are recursively applied to group 3D objects based on support, surround, and co-occurrence relations in a scene, encoding information about objects' spatial properties, semantics, and relative positioning with respect to other objects in the hierarchy. By training a variational autoencoder (VAE), the resulting fixed-length codes roughly follow a Gaussian distribution. A novel 3D scene can be generated hierarchically by the decoder from a randomly sampled code from the learned distribution. We coin our method GRAINS, for Generative Recursive autoencoders for INdoor Scenes. We demonstrate the capability of GRAINS to generate plausible and diverse 3D indoor scenes and compare with existing methods for 3D scene synthesis. We show applications of GRAINS including 3D scene modeling from 2D layouts, scene editing, and semantic scene segmentation via PointNet whose performance is boosted by the large quantity and variety of 3D scenes generated by our method.

关键词： 3D indoor scene generation recursive neural network variational autoencoder

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A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach

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KNOWLEDGE-BASED SYSTEMS 2023年 280卷

作者： Li, Zhiyuan Liang, Huawei Wang, Hanqi Zheng, Xiaokun Wang, Jian Zhou, Pengfei Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Engn Lab Intelligent Driving Technol & Appli Hefei 230031 Peoples R China Chinese Acad Sci Innovat Res Inst Robot & Intelligent Mfg Hefei 230031 Peoples R China

Accurate prediction of the future motion of surrounding vehicles is crucial for ensuring the safety of motion planning in autonomous vehicles. However, it is challenging to perform because of the complex interactions between vehicles. In this study, we propose a novel multi-modal vehicle trajectory prediction framework that utilizes a coarse-to-fine approach by first generating initial trajectory proposals with a conditional variational autoencoder (CVAE) and then refining them using the conditional diffusion model. We first address the problems of data sparsity and irregularity by converting the trajectory coordinates to the Frenet coordinate system. To enable the model to better distinguish between different features, we employ a temporal encoder to extract trajectory features and a long short-term memory (LSTM) network to extract lane features. The target lane evaluator is utilized to calculate the attention weights for each lane candidate, thereby generating more deterministic future trajectories. We then use the CVAE to generate initial multiple trajectory proposals based on the surrounding scene context and the trajectory features of the target vehicle. Finally, we formulate the trajectory refinement task as a reverse process of the conditional diffusion model, which effectively enhances the multi-modal trajectory proposals. Experiments conducted on Argoverse and nuScenes demonstrate that our method outperforms state-of-the-art methods in some evaluation metrics while achieving the optimal trade-off between predictive accuracy and efficiency. Field experiments conducted in urban environments further validate the effectiveness of the proposed approach. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Trajectory prediction Autonomous vehicles variational autoencoder Diffusion model

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Two-pronged feature reduction in spectral clustering with optimized landmark selection

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Rouhi, Alireza Bouyer, Asgarali Arasteh, Bahman Liu, Xiaoyang Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China

Spectral clustering is widely employed for clustering data points, particularly for non-linear and non-convex structures in high-dimensional spaces. However, it faces challenges due to the high computational cost of eigen decomposition operations and the performance limitations with high-dimensional data. In this paper, we introduce BVA_LSC, a novel spectral clustering algorithm designed to address these challenges. Firstly, we incorporate an advanced feature reduction stage utilizing Barnes-Hut t-SNE and a deep variational autoencoder (VAE) to efficiently reduce the dimensionality of the data, thereby accelerating eigen decomposition. Secondly, we propose an adaptive landmark selection strategy that combines the Grey Wolf Optimizer (GWO) with a novel objective function and K-harmonic means clustering. This strategy dynamically determines an optimal number of landmarks, enhancing the representativeness of the data and reducing the size of the similarity matrix. We assess the performance of our algorithm on various standard datasets, demonstrating its superiority over state-of-the-art methods in terms of accuracy and efficiency.

关键词： Spectral Clustering variational autoencoder Landmark Grey Wolf Optimization K-harmonic means Unsupervised Learning

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Modality encoded latent dataset for emotion recognition

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part2期79卷 000-000页

作者： Mert, Ahmet Bursa Tech Univ Dept Mechatron Engn Bursa Turkiye

variational autoencoder (VAE) is an unsupervised learning that represents high dimensional input data into normally distributed latent space. Multi-channel physiological signals, namely EEG and peripherals are mostly preferred for affective computing. The DEAP dataset is converted into multimodal latent dataset for emotion recognition in this study. 40-ch recordings of 32 participants are encoded to different modalities of peripherals and 32-ch EEG. First, short-time Fourier transform (STFT) is used to extract time-frequency (TF) distribution for training VAE. Thus, the localized components in the each channel of the modalities is converted to 100 -dimensional space using VAE. The proposed method is applied to each participant's recordings to obtain new latent encoded dataset. Within and between subject classification results using latent dataset are compared to the original data for peripheral, 32ch EEG and peripheral with EEG modalities. Naive Bayes (NB) classifier is used to evaluate the encoding performance of the 100-dimensional modalities, and compared to original results. The error rates of leave-one participant-out cross-validation (LOPO CV) 0.3322 and 0.3327 are yielded for high/low arousal and valence states while the originals are 0.349 and 0.382.

关键词： variational autoencoder Emotion recognition Multimodal data fusion Latent space

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Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期191卷

作者： Zhou, Haoxuan Lei, Zihao Zio, Enrico Wen, Guangrui Liu, Zimin Su, Yu Chen, Xuefeng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China MINES ParisPSL Univ Ctr Rech Risques & Crises CRC Sophia Antipolis France Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Politecn Milan Energy Dept Via Masa 34 I-20156 Milan Italy

Anomaly detection(AD) is an important task of machines' condition monitoring(CM). Data-driven policies can be used in a more intelligent way to achieve anomaly detection and effectively avoid the introduction of expert experience, thus having a broader scope of application. However, Machines like wind turbines often work under time-varying operating conditions(TVOCs), and the performance of traditional data-driven AD methods is significantly degraded because TVOCs can lead to "false alarms" and "missed alarms" in the implementation due to the monitoring data shifting caused by variation of operating conditions(OCs). To address this problem, this paper proposes a novel conditional feature disentanglement learning framework to solve the distur-bance in AD on account of entanglement between OCs and health states. The proposed approach performs conditional self-supervised AD by utilizing the variational autoencoder(VAE) and OCs information. Then, a feature disentanglement conditional VAE(FDCVAE) network is developed to realize the disentanglement of OCs and health states. Subsequently, An anomaly indicator(ANI) is constructed by the dimension reduction of the disentangled health state-related feature and combined with the statistics anomaly threshold for AD. Experiments on accelerated fatigue degradation of bearings under TVOCs validate the effectiveness of the proposed method and further demonstrate the superiority of the constructed ANI in eliminating TVOC interference, compared with common fault mechanisms-based and data-driven ANI. The proposed method not only achieves anomaly detection under TVOCs but also provides a new way for representation learning under variable working conditions in machine health management applications in the foreseeable future.

关键词： Time-varying operating conditions Anomaly detection Machines Feature disentanglement learning variational autoencoder Bearing

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Generative geomodeling based on flow responses in latent space

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期211卷 110177-110177页

作者： Jo, Suryeom Ahn, Seongin Park, Changhyup Kim, Jaejun Korea Inst Geosci & Mineral Resources GeoICT Convergence Res Team Daejeon 34132 South Korea Kangwon Natl Univ Dept Energy & Resources Engn Chunchon 24341 Kangwon South Korea Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea

This paper presents a new deep-learning-based generative method applicable to history matching without an inverse scheme. Multiple-point geostatistics is used to construct a prior population stochastically. A convolutional variational autoencoder (VAE) with probabilistic latent space is trained as the generative method, and kmeans clustering, nondominated sorting, and multilevel geomodel generations are performed based on flow responses. The applicability of the developed workflow was confirmed using a waterflooding problem with multiple wells in fluvial channel reservoirs. The VAE generates new geomodels based on the latent features and builds equiprobable models neighboring the representative models that reflect the observed production performance. The geomodels match the oil production profiles reliably as the steps progress and accurately forecast the water breakthrough time and liquid production trajectories. The density map of plausible geomodels explains reasonably the uncertainty of channel connectivity. The structural similarity index confirms that the generated geomodels become similar to the target reservoir and thus that the developed VAE-based framework creates geomodels that preserve geological realism. This proposed method involves relatively less time-consuming simulations without any inverse or optimization processes;nonetheless, it generates plausible geomodels in dimensionality-reduced latent space. The study methods and findings are thus applicable to scale-variant data integration and uncertainty assessment.

关键词： Generative model variational autoencoder Latent space History matching Uncertainty Flow response

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Characterization of porous membranes using artificial neural networks

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ACTA MATERIALIA 2023年 253卷

作者： Zhao, Yinghan Altschuh, Patrick Santoki, Jay Griem, Lars Tosato, Giovanna Selzer, Michael Koeppe, Arnd Nestler, Britta Karlsruhe Inst Technol Inst Appl Mat Microstruct Modelling & Simulat Str Forum 7 D-76131 Karlsruhe Germany Karlsruhe Univ Appl Sci Inst Digital Mat Moltkestr 30 D-76133 Karlsruhe Germany

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process-structure-property relationships with data-driven algorithms such as artificial neural networks. In this study, a variational autoencoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure-property relationship and solve the inverse problem of process-structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly.

关键词： Porous membranes Microstructure characterization Machine learning variational autoencoder Bayesian optimization

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