arXiv - PHYS - Data Analysis, Statistics and Probability最新文献_第9页

Learning dynamical models from stochastic trajectories 从随机轨迹学习动力学模型

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-04 DOI: arxiv-2406.02363

Pierre Ronceray

{"title":"Learning dynamical models from stochastic trajectories","authors":"Pierre Ronceray","doi":"arxiv-2406.02363","DOIUrl":"https://doi.org/arxiv-2406.02363","url":null,"abstract":"The dynamics of biological systems, from proteins to cells to organisms, is\u0000complex and stochastic. To decipher their physical laws, we need to bridge\u0000between experimental observations and theoretical modeling. Thanks to progress\u0000in microscopy and tracking, there is today an abundance of experimental\u0000trajectories reflecting these dynamical laws. Inferring physical models from\u0000noisy and imperfect experimental data, however, is challenging. Because there\u0000are no inference methods that are robust and efficient, model reconstruction\u0000from experimental trajectories is a bottleneck to data-driven biophysics. In\u0000this Thesis, I present a set of tools developed to bridge this gap and permit\u0000robust and universal inference of stochastic dynamical models from experimental\u0000trajectories. These methods are rooted in an information-theoretical framework\u0000that quantifies how much can be inferred from trajectories that are short,\u0000partial and noisy. They permit the efficient inference of dynamical models for\u0000overdamped and underdamped Langevin systems, as well as the inference of\u0000entropy production rates. I finally present early applications of these\u0000techniques, as well as future research directions.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252723","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

Inferring interaction potentials from stochastic particle trajectories 从随机粒子轨迹推断相互作用势

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-03 DOI: arxiv-2406.01522

Ella M. King, Megan C. Engel, Caroline Martin, Alp M. Sunol, Qian-Ze Zhu, Sam S. Schoenholz, Vinothan N. Manoharan, Michael P. Brenner

{"title":"Inferring interaction potentials from stochastic particle trajectories","authors":"Ella M. King, Megan C. Engel, Caroline Martin, Alp M. Sunol, Qian-Ze Zhu, Sam S. Schoenholz, Vinothan N. Manoharan, Michael P. Brenner","doi":"arxiv-2406.01522","DOIUrl":"https://doi.org/arxiv-2406.01522","url":null,"abstract":"Accurate interaction potentials between microscopic components such as\u0000colloidal particles or cells are crucial to understanding a range of processes,\u0000including colloidal crystallization, bacterial colony formation, and cancer\u0000metastasis. Even in systems where the precise interaction mechanisms are\u0000unknown, effective interactions can be measured to inform simulation and\u0000design. However, these measurements are difficult and time-intensive, and often\u0000require conditions that are drastically different from in situ conditions of\u0000the system of interest. Moreover, existing methods of measuring interparticle\u0000potentials rely on constraining a small number of particles at equilibrium,\u0000placing limits on which interactions can be measured. We introduce a method for\u0000inferring interaction potentials directly from trajectory data of interacting\u0000particles. We explicitly solve the equations of motion to find a form of the\u0000potential that maximizes the probability of observing a known trajectory. Our\u0000method is valid for systems both in and out of equilibrium, is well-suited to\u0000large numbers of particles interacting in typical system conditions, and does\u0000not assume a functional form of the interaction potential. We apply our method\u0000to infer the interactions of colloidal spheres from experimental data,\u0000successfully extracting the range and strength of a depletion interaction from\u0000the motion of the particles.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253349","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

Accelerator system parameter estimation using variational autoencoded latent regression 利用变异自动编码潜回归进行加速器系统参数估计

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-03 DOI: arxiv-2406.01532

Mahindra Rautela, Alan Williams, Alexander Scheinker

{"title":"Accelerator system parameter estimation using variational autoencoded latent regression","authors":"Mahindra Rautela, Alan Williams, Alexander Scheinker","doi":"arxiv-2406.01532","DOIUrl":"https://doi.org/arxiv-2406.01532","url":null,"abstract":"Particle accelerators are time-varying systems whose components are perturbed\u0000by external disturbances. Tuning accelerators can be a time-consuming process\u0000involving manual adjustment of multiple components, such as RF cavities, to\u0000minimize beam loss due to time-varying drifts. The high dimensionality of the\u0000system ($sim$100 amplitude and phase RF settings in the LANSCE accelerator)\u0000makes it difficult to achieve optimal operation. The time-varying drifts and\u0000the dimensionality make system parameter estimation a challenging optimization\u0000problem. In this work, we propose a Variational Autoencoded Latent Regression\u0000(VALeR) model for robust estimation of system parameters using 2D unique\u0000projections of a charged particle beam's 6D phase space. In VALeR, VAE projects\u0000the phase space projections into a lower-dimensional latent space, and a dense\u0000neural network maps the latent space onto the space of system parameters. The\u0000trained network can predict system parameters for unseen phase space\u0000projections. Furthermore, VALeR can generate new projections by randomly\u0000sampling the latent space of VAE and also estimate the corresponding system\u0000parameters.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252730","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

Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams 实现带电粒子束六维相空间时空动态的潜空间演化

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-03 DOI: arxiv-2406.01535

Mahindra Rautela, Alan Williams, Alexander Scheinker

{"title":"Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams","authors":"Mahindra Rautela, Alan Williams, Alexander Scheinker","doi":"arxiv-2406.01535","DOIUrl":"https://doi.org/arxiv-2406.01535","url":null,"abstract":"Addressing the charged particle beam diagnostics in accelerators poses a\u0000formidable challenge, demanding high-fidelity simulations in limited\u0000computational time. Machine learning (ML) based surrogate models have emerged\u0000as a promising tool for non-invasive charged particle beam diagnostics. Trained\u0000ML models can make predictions much faster than computationally expensive\u0000physics simulations. In this work, we have proposed a temporally structured\u0000variational autoencoder model to autoregressively forecast the spatiotemporal\u0000dynamics of the 15 unique 2D projections of 6D phase space of charged particle\u0000beam as it travels through the LANSCE linear accelerator. In the model, VAE\u0000embeds the phase space projections into a lower dimensional latent space. A\u0000long-short-term memory network then learns the temporal correlations in the\u0000latent space. The trained network can evolve the phase space projections across\u0000further modules provided the first few modules as inputs. The model predicts\u0000all the projections across different modules with low mean squared error and\u0000high structural similarity index.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252721","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

Predicting the fatigue life of asphalt concrete using neural networks 利用神经网络预测沥青混凝土的疲劳寿命

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-03 DOI: arxiv-2406.01523

Jakub Houlík, Jan Valentin, Václav Nežerka

{"title":"Predicting the fatigue life of asphalt concrete using neural networks","authors":"Jakub Houlík, Jan Valentin, Václav Nežerka","doi":"arxiv-2406.01523","DOIUrl":"https://doi.org/arxiv-2406.01523","url":null,"abstract":"Asphalt concrete's (AC) durability and maintenance demands are strongly\u0000influenced by its fatigue life. Traditional methods for determining this\u0000characteristic are both resource-intensive and time-consuming. This study\u0000employs artificial neural networks (ANNs) to predict AC fatigue life, focusing\u0000on the impact of strain level, binder content, and air-void content. Leveraging\u0000a substantial dataset, we tailored our models to effectively handle the wide\u0000range of fatigue life data, typically represented on a logarithmic scale. The\u0000mean square logarithmic error was utilized as the loss function to enhance\u0000prediction accuracy across all levels of fatigue life. Through comparative\u0000analysis of various hyperparameters, we developed a machine-learning model that\u0000captures the complex relationships within the data. Our findings demonstrate\u0000that higher binder content significantly enhances fatigue life, while the\u0000influence of air-void content is more variable, depending on binder levels.\u0000Most importantly, this study provides insights into the intricacies of using\u0000ANNs for modeling, showcasing their potential utility with larger datasets. The\u0000codes developed and the data used in this study are provided as open source on\u0000a GitHub repository, with a link included in the paper for full access.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252729","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

Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models 利用去噪扩散概率模型实现高能物理中探测器效应的普遍展开

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-03 DOI: arxiv-2406.01507

Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Martin Klassen, Taritree Wongjirad

引用次数: 0

SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory SwdFold：基于最优传输理论的重权重和展开方法

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-02 DOI: arxiv-2406.01635

Chu-Cheng Pan, Xiang Dong, Yu-Chang Sun, Ao-Yan Cheng, Ao-Bo Wang, Yu-Xuan Hu, Hao Cai

引用次数: 0

Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing 用于遥感卫星图像分类的 Kolmogorov-Arnold 网络

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-02 DOI: arxiv-2406.00600

Minjong Cheon

{"title":"Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing","authors":"Minjong Cheon","doi":"arxiv-2406.00600","DOIUrl":"https://doi.org/arxiv-2406.00600","url":null,"abstract":"In this research, we propose the first approach for integrating the\u0000Kolmogorov-Arnold Network (KAN) with various pre-trained Convolutional Neural\u0000Network (CNN) models for remote sensing (RS) scene classification tasks using\u0000the EuroSAT dataset. Our novel methodology, named KCN, aims to replace\u0000traditional Multi-Layer Perceptrons (MLPs) with KAN to enhance classification\u0000performance. We employed multiple CNN-based models, including VGG16,\u0000MobileNetV2, EfficientNet, ConvNeXt, ResNet101, and Vision Transformer (ViT),\u0000and evaluated their performance when paired with KAN. Our experiments\u0000demonstrated that KAN achieved high accuracy with fewer training epochs and\u0000parameters. Specifically, ConvNeXt paired with KAN showed the best performance,\u0000achieving 94% accuracy in the first epoch, which increased to 96% and remained\u0000consistent across subsequent epochs. The results indicated that KAN and MLP\u0000both achieved similar accuracy, with KAN performing slightly better in later\u0000epochs. By utilizing the EuroSAT dataset, we provided a robust testbed to\u0000investigate whether KAN is suitable for remote sensing classification tasks.\u0000Given that KAN is a novel algorithm, there is substantial capacity for further\u0000development and optimization, suggesting that KCN offers a promising\u0000alternative for efficient image analysis in the RS field.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"181 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252725","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

Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning 利用物理信息机器学习对超早期电池原型进行非破坏性降解模式解耦验证

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-01 DOI: arxiv-2406.00276

Shengyu Tao, Mengtian Zhang, Zixi Zhao, Haoyang Li, Ruifei Ma, Yunhong Che, Xin Sun, Lin Su, Xiangyu Chen, Zihao Zhou, Heng Chang, Tingwei Cao, Xiao Xiao, Yaojun Liu, Wenjun Yu, Zhongling Xu, Yang Li, Han Hao, Xuan Zhang, Xiaosong Hu, Guangmin ZHou

{"title":"Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning","authors":"Shengyu Tao, Mengtian Zhang, Zixi Zhao, Haoyang Li, Ruifei Ma, Yunhong Che, Xin Sun, Lin Su, Xiangyu Chen, Zihao Zhou, Heng Chang, Tingwei Cao, Xiao Xiao, Yaojun Liu, Wenjun Yu, Zhongling Xu, Yang Li, Han Hao, Xuan Zhang, Xiaosong Hu, Guangmin ZHou","doi":"arxiv-2406.00276","DOIUrl":"https://doi.org/arxiv-2406.00276","url":null,"abstract":"Manufacturing complexities and uncertainties have impeded the transition from\u0000material prototypes to commercial batteries, making prototype verification\u0000critical to quality assessment. A fundamental challenge involves deciphering\u0000intertwined chemical processes to characterize degradation patterns and their\u0000quantitative relationship with battery performance. Here we show that a\u0000physics-informed machine learning approach can quantify and visualize\u0000temporally resolved losses concerning thermodynamics and kinetics only using\u0000electric signals. Our method enables non-destructive degradation pattern\u0000characterization, expediting temperature-adaptable predictions of entire\u0000lifetime trajectories, rather than end-of-life points. The verification speed\u0000is 25 times faster yet maintaining 95.1% accuracy across temperatures. Such\u0000advances facilitate more sustainable management of defective prototypes before\u0000massive production, establishing a 19.76 billion USD scrap material recycling\u0000market by 2060 in China. By incorporating stepwise charge acceptance as a\u0000measure of the initial manufacturing variability of normally identical\u0000batteries, we can immediately identify long-term degradation variations. We\u0000attribute the predictive power to interpreting machine learning insights using\u0000material-agnostic featurization taxonomy for degradation pattern decoupling.\u0000Our findings offer new possibilities for dynamic system analysis, such as\u0000battery prototype degradation, demonstrating that complex pattern evolutions\u0000can be accurately predicted in a non-destructive and data-driven fashion by\u0000integrating physics-informed machine learning.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253123","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

Error evaluation of partial scattering functions obtained from contrast variation small-angle neutron scattering 从对比变化小角中子散射中获得的部分散射函数的误差评估

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-01 DOI: arxiv-2406.00311

Koichi Mayumi, Shinya Miyajima, Ippei Obayashi, Kazuaki Tanaka

引用次数: 0