APL Machine Learning最新文献_第2页

Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation 用改进的 Hessian 近似法进行神经网络表观不确定性估计的稀疏子网络推理

APL Machine Learning Pub Date : 2024-04-05 DOI: 10.1063/5.0193951

Yinsong Chen, Samson Yu, J. Eshraghian, Chee Peng Lim

{"title":"Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation","authors":"Yinsong Chen, Samson Yu, J. Eshraghian, Chee Peng Lim","doi":"10.1063/5.0193951","DOIUrl":"https://doi.org/10.1063/5.0193951","url":null,"abstract":"Despite significant advances in deep neural networks across diverse domains, challenges persist in safety-critical contexts, including domain shift sensitivity and unreliable uncertainty estimation. To address these issues, this study investigates Bayesian learning for uncertainty handling in modern neural networks. However, the high-dimensional, non-convex nature of the posterior distribution poses practical limitations for epistemic uncertainty estimation. The Laplace approximation, as a cost-efficient Bayesian method, offers a practical solution by approximating the posterior as a multivariate normal distribution but faces computational bottlenecks in precise covariance matrix computation and storage. This research employs subnetwork inference, utilizing only a subset of the parameter space for Bayesian inference. In addition, a Kronecker-factored and low-rank representation is explored to reduce space complexity and computational costs. Several corrections are introduced to converge the approximated curvature to the exact Hessian matrix. Numerical results demonstrate the effectiveness and competitiveness of this method, whereas qualitative experiments highlight the impact of Hessian approximation granularity and parameter space utilization in Bayesian inference on mitigating overconfidence in predictions and obtaining high-quality uncertainty estimates.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"35 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imaging in double-casing wells with convolutional neural network based on inception module 利用基于起始模块的卷积神经网络进行双套管井成像

APL Machine Learning Pub Date : 2024-04-05 DOI: 10.1063/5.0191452

Siqi Zhang, Zhoumo Zeng, Xiaocen Wang, Shili Chen, Yang Liu

{"title":"Imaging in double-casing wells with convolutional neural network based on inception module","authors":"Siqi Zhang, Zhoumo Zeng, Xiaocen Wang, Shili Chen, Yang Liu","doi":"10.1063/5.0191452","DOIUrl":"https://doi.org/10.1063/5.0191452","url":null,"abstract":"The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"26 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing nonlinear conductive characteristic of TiO2/HfO2 memristor crossbar for implementing parallel vector–matrix multiplication 利用 TiO2/HfO2 晶闸管交叉棒的非线性传导特性实现并行矢量矩阵乘法

APL Machine Learning Pub Date : 2024-04-04 DOI: 10.1063/5.0195190

Wei Wei, Cong Wang, Chen Pan, Xing-Jian Yangdong, Zaizheng Yang, Yuekun Yang, Bin Cheng, Shi-Jun Liang, Feng Miao

{"title":"Harnessing nonlinear conductive characteristic of TiO2/HfO2 memristor crossbar for implementing parallel vector–matrix multiplication","authors":"Wei Wei, Cong Wang, Chen Pan, Xing-Jian Yangdong, Zaizheng Yang, Yuekun Yang, Bin Cheng, Shi-Jun Liang, Feng Miao","doi":"10.1063/5.0195190","DOIUrl":"https://doi.org/10.1063/5.0195190","url":null,"abstract":"Memristor crossbar arrays are expected to achieve highly energy-efficient neuromorphic computing via implementing parallel vector–matrix multiplication (VMM) in situ. The similarities between memristors and neural synapses offer opportunities for realizing hardware-based brain-inspired computing, such as spike neural networks. However, the nonlinear I–V characteristics of the memristors limit the implementation of parallel VMM on passive memristor crossbar arrays. In our work, we propose to utilize differential conductance as a synaptic weight to implement linear VMM operations on a passive memristor array in parallel. We fabricated a TiO2/HfO2 memristor crossbar array, in which differential-conductance-based synaptic weight exhibits plasticity, nonvolatility, multi-states, and tunable ON/OFF ratio. The noise-dependent accuracy performance of VMM operations based on the proposed approach was evaluated, offering an optimization guideline. Furthermore, we demonstrated a spike neural network circuit capable of processing small spiking signals through the differential-conductance-based synapses. The experimental results showcase effective space-coded and time-coded spike pattern recognition. Importantly, our work opens up new possibilities for the development of passive memristor arrays, leading to increased energy and area efficiency in brain-inspired chips.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"33 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network 利用图卷积网络研究高熵合金的二氧化碳还原吸附点

APL Machine Learning Pub Date : 2024-04-03 DOI: 10.1063/5.0198043

H. Oliaei, N. Aluru

{"title":"Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network","authors":"H. Oliaei, N. Aluru","doi":"10.1063/5.0198043","DOIUrl":"https://doi.org/10.1063/5.0198043","url":null,"abstract":"Carbon dioxide reduction is a major step toward building a cleaner and safer environment. There is a surge of interest in exploring high-entropy alloys (HEAs) as active catalysts for CO2 reduction; however, so far, it is mainly limited to quinary HEAs. Inspired by the successful synthesis of octonary and denary HEAs, herein, the CO2 reduction reaction (CO2RR) performance of an HEA composed of Ag, Au, Cu, Pd, Pt, Co, Ga, Ni, and Zn is studied by developing a high-fidelity graph neural network (GNN) framework. Within this framework, the adsorption site geometry and physics are employed through the featurization of elements. Particularly, featurization is performed using various intrinsic properties, such as electronegativity and atomic radius, to enable not only the supervised learning of CO2RR performance descriptors, namely, CO and H adsorption energies, but also the learning of adsorption physics and generalization to unseen metals and alloys. The developed model evaluates the adsorption strength of ∼3.5 and ∼0.4 billion possible sites for CO and H, respectively. Despite the enormous space of the AgAuCuPdPtCoGaNiZn alloy and the rather small size of the training data, the GNN framework demonstrated high accuracy and good robustness. This study paves the way for the rapid screening and intelligent synthesis of CO2RR-active and selective HEAs.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"749 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational synthesis of a new generation of 2D-based perovskite quantum materials 新一代基于二维的过氧化物量子材料的计算合成

APL Machine Learning Pub Date : 2024-04-02 DOI: 10.1063/5.0189497

C. Ekuma

引用次数: 0

VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions VERI-D：用于在不同光照条件下对受损汽车进行多摄像头车辆再识别的新数据集和方法

APL Machine Learning Pub Date : 2024-03-01 DOI: 10.1063/5.0183408

Shao Liu, S. Agaian

{"title":"VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions","authors":"Shao Liu, S. Agaian","doi":"10.1063/5.0183408","DOIUrl":"https://doi.org/10.1063/5.0183408","url":null,"abstract":"Vehicle re-identification (V-ReID) is a critical task that aims to match the same vehicle across images from different camera viewpoints. The previous studies have leveraged attribute clues, such as color, model, and license plate, to enhance the V-ReID performance. However, these methods often lack effective interaction between the global–local features and the final V-ReID objective. Moreover, they do not address the challenging issues in real-world scenarios, such as high viewpoint variations, extreme illumination conditions, and car appearance changes (e.g., due to damage or wrong driving). We propose a novel framework to tackle these problems and advance the research in V-ReID, which can handle various types of car appearance changes and achieve robust V-ReID under varying lighting conditions. Our main contributions are as follows: (i) we propose a new Re-ID architecture named global–local self-attention network, which integrates local information into the feature learning process and enhances the feature representation for V-ReID and (ii) we introduce a novel damaged vehicle Re-ID dataset called VERI-D, which is the first publicly available dataset that focuses on this challenging yet practical scenario. The dataset contains both natural and synthetic images of damaged vehicles captured from multiple camera viewpoints and under different lighting conditions. (iii) We conduct extensive experiments on the VERI-D dataset and demonstrate the effectiveness of our approach in addressing the challenges associated with damaged vehicle re-identification. We also compare our method to several state-of-the-art V-ReID methods and show its superiority.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs 利用深度学习探测时间序列显微照片中辐照诱发的缺陷

APL Machine Learning Pub Date : 2024-03-01 DOI: 10.1063/5.0186046

K. Burns, Kayvon Tadj, Tarun Allaparti, Liliana Arias, Nan Li, A. Aitkaliyeva, Amit Misra, M. Scott, Khalid Hattar

{"title":"Deep learning-enabled probing of irradiation-induced defects in time-series micrographs","authors":"K. Burns, Kayvon Tadj, Tarun Allaparti, Liliana Arias, Nan Li, A. Aitkaliyeva, Amit Misra, M. Scott, Khalid Hattar","doi":"10.1063/5.0186046","DOIUrl":"https://doi.org/10.1063/5.0186046","url":null,"abstract":"Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"543 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140280958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the mechanical properties of Cantor-like alloys with Bayesian optimization 用贝叶斯优化法改善康托合金的机械性能

APL Machine Learning Pub Date : 2024-03-01 DOI: 10.1063/5.0179844

Valtteri Torsti, T. Mäkinen, Silvia Bonfanti, J. Koivisto, Mikko J. Alava

{"title":"Improving the mechanical properties of Cantor-like alloys with Bayesian optimization","authors":"Valtteri Torsti, T. Mäkinen, Silvia Bonfanti, J. Koivisto, Mikko J. Alava","doi":"10.1063/5.0179844","DOIUrl":"https://doi.org/10.1063/5.0179844","url":null,"abstract":"The search for better compositions in high entropy alloys is a formidable challenge in materials science. Here, we demonstrate a systematic Bayesian optimization method to enhance the mechanical properties of the paradigmatic five-element Cantor alloy in silico. This method utilizes an automated loop with an online database, a Bayesian optimization algorithm, thermodynamic modeling, and molecular dynamics simulations. Starting from the equiatomic Cantor composition, our approach optimizes the relative fractions of its constituent elements, searching for better compositions while maintaining the thermodynamic phase stability. With 24 steps, we find Fe21Cr20Mn5Co20Ni34 with a yield stress improvement of 58%, and with 72 steps, we find Fe6Cr22Mn5Co32Ni35 where the yield stress has improved by 74%. These optimized compositions correspond to Ni-rich medium entropy alloys with enhanced mechanical properties and superior face-centered-cubic phase stability compared to the traditional equiatomic Cantor alloy. The automatic approach devised here paves the way for designing high entropy alloys with tailored properties, opening avenues for numerous potential applications.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"269 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-agnostic inverse design using transfer matrices 利用传递矩阵进行物理无关的逆设计

APL Machine Learning Pub Date : 2024-02-28 DOI: 10.1063/5.0179457

Nathaniel Morrison, Shuaiwei Pan, Eric Y. Ma

引用次数: 0

Training self-learning circuits for power-efficient solutions 培训自学习电路，实现高能效解决方案

APL Machine Learning Pub Date : 2024-02-27 DOI: 10.1063/5.0181382

M. Stern, Sam Dillavou, Dinesh Jayaraman, D. Durian, Andrea J. Liu

引用次数: 0