APL Machine Learning最新文献_第2页

Bring memristive in-memory computing into general-purpose machine learning: A perspective 将记忆性内存计算引入通用机器学习:一个视角

APL Machine Learning Pub Date : 2023-10-11 DOI: 10.1063/5.0167743

Houji Zhou, Jia Chen, Jiancong Li, Ling Yang, Yi Li, Xiangshui Miao

引用次数: 0

Autoregressive transformers for data-driven spatiotemporal learning of turbulent flows 用于湍流数据驱动时空学习的自回归变压器

APL Machine Learning Pub Date : 2023-10-11 DOI: 10.1063/5.0152212

Aakash Patil, Jonathan Viquerat, Elie Hachem

{"title":"Autoregressive transformers for data-driven spatiotemporal learning of turbulent flows","authors":"Aakash Patil, Jonathan Viquerat, Elie Hachem","doi":"10.1063/5.0152212","DOIUrl":"https://doi.org/10.1063/5.0152212","url":null,"abstract":"A convolutional encoder–decoder-based transformer model is proposed for autoregressively training on spatiotemporal data of turbulent flows. The prediction of future fluid flow fields is based on the previously predicted fluid flow field to ensure long-term predictions without diverging. A combination of convolutional neural networks and transformer architecture is utilized to handle both the spatial and temporal dimensions of the data. To assess the performance of the model, a priori assessments are conducted, and significant agreements are found with the ground truth data. The a posteriori predictions, which are generated after a considerable number of simulation steps, exhibit predicted variances. The autoregressive training and prediction of a posteriori states are deemed crucial steps toward the development of more complex data-driven turbulence models and simulations. The highly nonlinear and chaotic dynamics of turbulent flows can be handled by the proposed model, and accurate predictions over long time horizons can be generated. Overall, the potential of using deep learning techniques to improve the accuracy and efficiency of turbulence modeling and simulation is demonstrated by this approach. The proposed model can be further optimized and extended to incorporate additional physics and boundary conditions, paving the way for more realistic simulations of complex fluid dynamics.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136057480","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: 2

KoopmanLab: Machine learning for solving complex physics equations KoopmanLab:解决复杂物理方程的机器学习

APL Machine Learning Pub Date : 2023-09-01 DOI: 10.1063/5.0157763

Wei Xiong, Muyuan Ma, Xiaomeng Huang, Ziyang Zhang, Pei Sun, Yang Tian

{"title":"KoopmanLab: Machine learning for solving complex physics equations","authors":"Wei Xiong, Muyuan Ma, Xiaomeng Huang, Ziyang Zhang, Pei Sun, Yang Tian","doi":"10.1063/5.0157763","DOIUrl":"https://doi.org/10.1063/5.0157763","url":null,"abstract":"Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator (KNO) family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the KNO, a kind of mesh-independent neural-network-based PDE solvers developed following the dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes, while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier–Stokes equation and the Bateman–Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate datasets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298336","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

Experimental realization of a quantum classification: Bell state measurement via machine learning 量子分类的实验实现:基于机器学习的贝尔态测量

APL Machine Learning Pub Date : 2023-09-01 DOI: 10.1063/5.0149414

Qing-Yuan Wu, Zhe Meng, Xiao-Xiao Chen, Jian Li, Jia-Zhi Yang, An-Ning Zhang

{"title":"Experimental realization of a quantum classification: Bell state measurement via machine learning","authors":"Qing-Yuan Wu, Zhe Meng, Xiao-Xiao Chen, Jian Li, Jia-Zhi Yang, An-Ning Zhang","doi":"10.1063/5.0149414","DOIUrl":"https://doi.org/10.1063/5.0149414","url":null,"abstract":"The Bell state is a crucial resource for the realization of quantum information tasks, and when combined with orbital angular momentum (OAM), it enables a high-dimensional Hilbert space, which is essential for high-capacity quantum communication. In this study, we demonstrate the recognition of OAM Bell states using interference patterns generated by a classical light source and a single-photon source from a Sagnac interferometer-based OAM Bell state evolution device. The interference patterns exhibit a one-to-one correspondence with the input Bell states, providing conclusive evidence for the full recognition of OAM Bell states. Furthermore, we introduce machine learning to the field of Bell state recognition by proposing a neural network model capable of accurately recognizing higher order single-photon OAM Bell states, even in the undersampling case. In particular, the model’s training set includes interference patterns of OAM Bell states generated by classical light sources, yet it is able to recognize single-photon OAM Bell states with high accuracy, without relying on quantum resources during training. Our innovative application of neural networks to the recognition of single-photon OAM Bell states not only circumvents the resource consumption and experimental difficulties associated with quantum light sources but also facilitates the study of OAM-based quantum information.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299021","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

Tutorials at APL Machine Learning: To share, to envision, and to help others learn APL机器学习教程:分享，设想，并帮助他人学习

APL Machine Learning Pub Date : 2023-09-01 DOI: 10.1063/5.0175787

Shijing Sun

引用次数: 0

Accelerated and interpretable prediction of local properties in composites 复合材料局部性能的加速和可解释预测

APL Machine Learning Pub Date : 2023-09-01 DOI: 10.1063/5.0156517

Shengtong Zhang, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley

{"title":"Accelerated and interpretable prediction of local properties in composites","authors":"Shengtong Zhang, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley","doi":"10.1063/5.0156517","DOIUrl":"https://doi.org/10.1063/5.0156517","url":null,"abstract":"The localized stress and strain field simulation results are critical for understanding the mechanical properties of materials, such as strength and toughness. However, applying off-the-shelf machine learning or deep learning methods to a digitized microstructure restricts the image samples to be of a fixed size and also lacks interpretability. Additionally, existing methods that utilize deep learning models to solve boundary value problems require retraining the model for each set of boundary conditions. To address these limitations, we propose a customized Pixel-Wise Convolutional Neural Network (PWCNN) to make fast predictions of stress and strain fields pixel-by-pixel under different loading conditions and for a wide range of composite microstructures of any size (e.g., much larger or smaller than the sample on which the PWCNN is trained). Through numerical experiments, we show that our PWCNN model serves as an alternative approach to numerical solution methods, such as finite element analysis, but is computationally more efficient, and the prediction errors on the test microstructure are around 5%. Moreover, we also propose an interpretable machine learning framework to facilitate the scientific discovery of why certain microstructures have better or worse performance than others, which has important implications in the design of composite microstructures in advanced manufacturing.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135348660","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

Flexible optoelectronic synaptic transistors for neuromorphic visual systems 用于神经形态视觉系统的柔性光电突触晶体管

APL Machine Learning Pub Date : 2023-08-28 DOI: 10.1063/5.0163926

Xiao Liu, Dongke Li, Yue Wang, Deren Yang, X. Pi

引用次数: 0

Deep ensemble inverse model for image-based estimation of solar cell parameters 基于图像估计太阳电池参数的深度集合反演模型

APL Machine Learning Pub Date : 2023-07-28 DOI: 10.1063/5.0139707

M. Battaglia, E. Comi, T. Stadelmann, R. Hiestand, B. Ruhstaller, E. Knapp

{"title":"Deep ensemble inverse model for image-based estimation of solar cell parameters","authors":"M. Battaglia, E. Comi, T. Stadelmann, R. Hiestand, B. Ruhstaller, E. Knapp","doi":"10.1063/5.0139707","DOIUrl":"https://doi.org/10.1063/5.0139707","url":null,"abstract":"Physical models can help improve solar cell efficiency during the design phase and for quality control after the fabrication process. We present a data-driven approach to inverse modeling that can predict the underlying parameters of a finite element method solar cell model based on an electroluminescence (EL) image of a solar cell with known cell geometry and laser scribed defects. For training the inverse model, 75 000 synthetic EL images were generated with randomized parameters of the physical cell model. We combine 17 deep convolutional neural networks based on a modified VGG19 architecture into a deep ensemble to add uncertainty estimates. Using the silicon solar cell model, we show that such a novel approach to data-driven statistical inverse modeling can help apply recent developments in deep learning to new engineering applications that require real-time parameterizations of physical models augmented by confidence intervals. The trained network was tested on four different physical solar cell samples, and the estimated parameters were used to create the corresponding model representations. Resimulations of the measurements yielded relative deviations of the calculated and the measured junction voltage values of 0.2% on average with a maximum of 10%, demonstrating the validity of the approach.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130988508","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: 1

Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach 多层陶瓷电容器(mlcc)失效时间的改进预测:基于物理的机器学习方法

APL Machine Learning Pub Date : 2023-07-26 DOI: 10.1063/5.0158360

P. Yousefian, Alireza Sepehrinezhad, A. V. van Duin, C. Randall

{"title":"Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach","authors":"P. Yousefian, Alireza Sepehrinezhad, A. V. van Duin, C. Randall","doi":"10.1063/5.0158360","DOIUrl":"https://doi.org/10.1063/5.0158360","url":null,"abstract":"Multilayer ceramic capacitors (MLCC) play a vital role in electronic systems, and their reliability is of critical importance. The ongoing advancement in MLCC manufacturing has improved capacitive volumetric density for both low and high voltage devices; however, concerns about long-term stability under higher fields and temperatures are always a concern, which impact their reliability and lifespan. Consequently, predicting the mean time to failure (MTTF) for MLCCs remains a challenge due to the limitations of existing models. In this study, we develop a physics-based machine learning approach using the eXtreme Gradient Boosting method to predict the MTTF of X7R MLCCs under various temperature and voltage conditions. We employ a transfer learning framework to improve prediction accuracy for test conditions with limited data and to provide predictions for test conditions where no experimental data exists. We compare our model with the conventional Eyring model (EM) and, more recently, the tipping point model (TPM) in terms of accuracy and performance. Our results show that the machine learning model consistently outperforms both the EM and TPM, demonstrating superior accuracy and stability across different conditions. Our model also exhibits a reliable performance for untested voltage and temperature conditions, making it a promising approach for predicting MTTF in MLCCs.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176593","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: 1

3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon 基于三维CNN和梯度cam的多晶硅中位错团簇生成预测可视化

APL Machine Learning Pub Date : 2023-07-24 DOI: 10.1063/5.0156044

Kyoka Hara, T. Kojima, K. Kutsukake, H. Kudo, N. Usami

{"title":"3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon","authors":"Kyoka Hara, T. Kojima, K. Kutsukake, H. Kudo, N. Usami","doi":"10.1063/5.0156044","DOIUrl":"https://doi.org/10.1063/5.0156044","url":null,"abstract":"We propose a machine learning-based technique to address the crystallographic characteristics responsible for the generation of crystal defects. A convolutional neural network was trained with pairs of optical images that display the characteristics of the crystal and photoluminescence images that show the distributions of crystal defects. The model was trained to predict the existence of crystal defects at the center pixel of the given image from its optical features. Prediction accuracy and separability were enhanced by feeding three-dimensional data and data augmentation. The prediction was successful with a high area under the curve of over 0.9 in a receiver operating characteristic curve. Likelihood maps showing the distributions of the predicted defects are in good resemblance with the correct distributions. Using the trained model, we visualized the most important regions to the predicted class by gradient-based class activation mapping. The extracted regions were found to contain mostly particular grains where the grain boundaries changed greatly due to crystal growth and clusters of small grains. This technique is beneficial in providing a rapid and statistical analysis of various crystal characteristics because the features of optical images are often complex and difficult to interpret. The interpretations can help us understand the physics of crystal growth and the effects of crystallographic characteristics on the generation of detrimental defects. We believe that this technique will contribute to the development of a better fabrication process for high-performance multicrystalline materials.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112515","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