With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses.
A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research
Department of Biostatistics, University of Arkansas for Medical Sciences（生物统计学系，亚拉巴马州医学科学大学）；Arkansas Children’s Research Institute（亚拉巴马州儿童研究研究所）；Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington（流行病学与生物统计学系，印第安纳大学公共健康学院-布卢明顿分校）；Department of Psychology, Emory University（心理学系，埃默里大学）
针对多标签数据中标签频率差异大且存在依赖关系的问题，提出一种基于多元伯努利分布的加权采样算法，通过估计标签组合权重实现目标分布特征，并在Web of Science研究文章数据上验证了其增强少数类别代表性的效果。
D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching
Sibley School of Mechanical and Aerospace Engineering, Cornell University（科尼尔大学机械与航空航天工程学院）；Department of Aerospace and Mechanical Engineering, University of Notre Dame（诺丁汉大学航空航天与机械工程系）
RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
Department of Civil and Environmental Engineering, Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Statistics and Data Science, Yale University（耶鲁大学统计与数据科学系）
