III: Small: DeepRep: Unsupervised Deep Representation Learning for Scientific Data Analysis and Visualization

从数据中学习特征或表示是数据挖掘和机器学习的长期目标。在科学可视化中，特征定义通常是特定于应用程序的，在许多情况下，它们是模糊的，甚至是未知的。表示学习通常是通向有效的科学数据分析和可视化(SDAV)的第一步，也是关键的一步。随着科学模拟数据的规模和复杂性不断增长，这一步骤变得越来越重要和必要。三十多年来，人工特征工程一直是科学可视化的标准实践。随着人工智能和机器学习的蓬勃发展，利用深度神经网络进行自动特征发现已成为一种有前途的可靠替代方案。该项目的总体目标是开发DeepRep，这是一个系统的深度表征学习框架。这些成果将提供一种范式转换，以最好地表示抽象特征空间中的科学数据，帮助科学家更好地理解各种物理、化学和医学现象，如气候、燃烧和心血管应用中的现象。因此，这个项目符合国家利益，正如NSF的使命所述：促进科学进步；促进国民健康、繁荣和福祉。SDAV主要处理未标记的数据。因此，项目团队将研究无监督学习技术，并探索它们在学习抽象、深层和表现力特征方面的应用。所提出的框架考虑了广泛的输入，包括三维标量和矢量数据及其视觉表示(即线、面和子体)。具体地说，该团队将研究不同的无监督深度表示学习技术，包括分布式学习、解缠学习和自我监督学习。DeepRep项目旨在展示它们在各种后续SDAV任务中的实用性，如降维、数据聚类、代表性选择、异常检测、数据分类和数据生成。提出的研究包括四个主要任务：(1)用于体数据及其视觉表示的分布式学习的自动编码器；(2)用于表面数据表示学习的图卷积网络，以支持节点级和图级操作；(3)通过分离学习从独立特征生成集成数据；(4)通过对比学习实现稳健数据表示的自监督解决方案。此外，该团队将使用多级别指标进行全面的客观和主观评估，以评估框架的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Learning features or representations from data is a longstanding goal of data mining and machine learning. In scientific visualization, feature definitions are usually application-specific, and in many cases, they are vague or even unknown. Representation learning is often the first and crucial step toward effective scientific data analysis and visualization (SDAV). This step has become increasingly important and necessary as the size and complexity of scientific simulation data continue to grow. For more than three decades, manual feature engineering has been the standard practice in scientific visualization. With the thriving of AI and machine learning, leveraging deep neural networks for automatic feature discovery has emerged as a promising and reliable alternative. The overarching goal of this project is to develop DeepRep, a systematic deep representation learning framework for SDAV. The outcomes will provide a paradigm shift to best represent scientific data in the abstract feature space, helping scientists better understand various physical, chemical, and medical phenomena such as those from climate, combustion, and cardiovascular applications. This project thus serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national health, prosperity, and welfare.SDAV mainly deals with unlabeled data. Therefore, the project team will investigate unsupervised learning techniques and explore their uses in learning abstract, deep, and expressive features. The proposed framework considers a broad range of inputs, including three-dimensional scalar and vector data and their visual representations (i.e., line, surface, and subvolume). Specifically, the team will study different unsupervised deep representation learning techniques, including distributed learning, disentangled learning, and self-supervised learning. The DeepRep project aims to demonstrate their utility in various subsequent SDAV tasks, such as dimensionality reduction, data clustering, representative selection, anomaly detection, data classification, and data generation. The proposed research includes four primary tasks: (1) autoencoders for distributed learning of volumetric data and their visual representations, (2) graph convolutional networks for representation learning of surface data to support node-level and graph-level operations, (3) ensemble data generation from independent features via disentangled learning, and (4) self-supervised solutions for robust data representation via contrastive learning. Furthermore, the team will perform comprehensive objective and subjective evaluations using multilevel metrics to evaluate the framework's effectiveness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.