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Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators 基于独立EnKF估计的样本平均值的多水平集成卡尔曼滤波
Foundations of data science (Springfield, Mo.) Pub Date : 2020-02-02 DOI: 10.3934/fods.2020017
Håkon Hoel, G. Shaimerdenova, R. Tempone
{"title":"Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators","authors":"Håkon Hoel, G. Shaimerdenova, R. Tempone","doi":"10.3934/fods.2020017","DOIUrl":"https://doi.org/10.3934/fods.2020017","url":null,"abstract":"We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44833004","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: 13
Index 指数
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.013
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引用次数: 0
Introduction 介绍
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.001
{"title":"Introduction","authors":"","doi":"10.1017/9781108755528.001","DOIUrl":"https://doi.org/10.1017/9781108755528.001","url":null,"abstract":"","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/9781108755528.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43733045","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
High-Dimensional Space 高维空间
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.002
{"title":"High-Dimensional Space","authors":"","doi":"10.1017/9781108755528.002","DOIUrl":"https://doi.org/10.1017/9781108755528.002","url":null,"abstract":"","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/9781108755528.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146576","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: 4
Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models 主题模型，非负矩阵分解，隐马尔可夫模型和图形模型
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.009
{"title":"Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models","authors":"","doi":"10.1017/9781108755528.009","DOIUrl":"https://doi.org/10.1017/9781108755528.009","url":null,"abstract":"","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/9781108755528.009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44986203","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
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization 融合数据同化、机器学习和期望最大化的混沌动力学贝叶斯推理
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-17 DOI: 10.3934/fods.2020004
M. Bocquet, J. Brajard, A. Carrassi, Laurent Bertino
{"title":"Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization","authors":"M. Bocquet, J. Brajard, A. Carrassi, Laurent Bertino","doi":"10.3934/fods.2020004","DOIUrl":"https://doi.org/10.3934/fods.2020004","url":null,"abstract":"The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics. To achieve such inference from the observations over long time series, it has been suggested to combine data assimilation and machine learning in several ways. We show how to unify these approaches from a Bayesian perspective using expectation-maximization and coordinate descents. In doing so, the model, the state trajectory and model error statistics are estimated all together. Implementations and approximations of these methods are discussed. Finally, we numerically and successfully test the approach on two relevant low-order chaotic models with distinct identifiability.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49478111","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: 75
Mean-field and kinetic descriptions of neural differential equations 神经微分方程的平均场和动力学描述
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-07 DOI: 10.3934/fods.2022007
M. Herty, T. Trimborn, G. Visconti
{"title":"Mean-field and kinetic descriptions of neural differential equations","authors":"M. Herty, T. Trimborn, G. Visconti","doi":"10.3934/fods.2022007","DOIUrl":"https://doi.org/10.3934/fods.2022007","url":null,"abstract":"Nowadays, neural networks are widely used in many applications as artificial intelligence models for learning tasks. Since typically neural networks process a very large amount of data, it is convenient to formulate them within the mean-field and kinetic theory. In this work we focus on a particular class of neural networks, i.e. the residual neural networks, assuming that each layer is characterized by the same number of neurons begin{document}$ N $end{document}, which is fixed by the dimension of the data. This assumption allows to interpret the residual neural network as a time-discretized ordinary differential equation, in analogy with neural differential equations. The mean-field description is then obtained in the limit of infinitely many input data. This leads to a Vlasov-type partial differential equation which describes the evolution of the distribution of the input data. We analyze steady states and sensitivity with respect to the parameters of the network, namely the weights and the bias. In the simple setting of a linear activation function and one-dimensional input data, the study of the moments provides insights on the choice of the parameters of the network. Furthermore, a modification of the microscopic dynamics, inspired by stochastic residual neural networks, leads to a Fokker-Planck formulation of the network, in which the concept of network training is replaced by the task of fitting distributions. The performed analysis is validated by artificial numerical simulations. In particular, results on classification and regression problems are presented.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42109967","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: 4
Topological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry 基于集合卡尔曼速度法的亚细胞运动拓扑重建
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-01 DOI: 10.3934/fods.2020007
Le Yin, Ioannis Sgouralis, V. Maroulas
{"title":"Topological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry","authors":"Le Yin, Ioannis Sgouralis, V. Maroulas","doi":"10.3934/fods.2020007","DOIUrl":"https://doi.org/10.3934/fods.2020007","url":null,"abstract":"Microscopy imaging of plant cells allows the elaborate analysis of sub-cellular motions of organelles. The large video data set can be efficiently analyzed by automated algorithms. We develop a novel, data-oriented algorithm, which can track organelle movements and reconstruct their trajectories on stacks of image data. Our method proceeds with three steps: (ⅰ) identification, (ⅱ) localization, and (ⅲ) linking. This method combines topological data analysis and Ensemble Kalman Filtering, and does not assume a specific motion model. Application of this method on simulated data sets shows an agreement with ground truth. We also successfully test our method on real microscopy data.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247921","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
Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems 求解解析延拓问题的随机优化的随机梯度下降算法
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-01 DOI: 10.3934/fods.2020001
F. Bao, T. Maier
{"title":"Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems","authors":"F. Bao, T. Maier","doi":"10.3934/fods.2020001","DOIUrl":"https://doi.org/10.3934/fods.2020001","url":null,"abstract":"We propose a stochastic gradient descent based optimization algorithm to solve the analytic continuation problem in which we extract real frequency spectra from imaginary time Quantum Monte Carlo data. The procedure of analytic continuation is an ill-posed inverse problem which is usually solved by regularized optimization methods, such like the Maximum Entropy method, or stochastic optimization methods. The main contribution of this work is to improve the performance of stochastic optimization approaches by introducing a supervised stochastic gradient descent algorithm to solve a flipped inverse system which processes the random solutions obtained by a type of Fast and Efficient Stochastic Optimization Method.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247865","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: 6
Hierarchical approximations for data reduction and learning at multiple scales 多尺度下数据约简和学习的层次近似
Foundations of data science (Springfield, Mo.) Pub Date : 2020-01-01 DOI: 10.3934/fods.2020008
P. Shekhar, A. Patra
{"title":"Hierarchical approximations for data reduction and learning at multiple scales","authors":"P. Shekhar, A. Patra","doi":"10.3934/fods.2020008","DOIUrl":"https://doi.org/10.3934/fods.2020008","url":null,"abstract":"This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations are presented, along with a well chosen set of applications. Results show the performance of the approach as a data reduction mechanism for both synthetic (univariate and multivariate) and a real dataset (geo-spatial). The sparse representation generated is shown to efficiently reconstruct data and minimize error in prediction. The approach is also shown to generalize well to unseen samples, extending its prospective application to statistical learning problems.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247939","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: 6
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