OAC Core: Small: Scalable Non-linear Dimensionality Reduction Methods to Accelerate Scientific Discovery

OAC 核心：小型：加速科学发现的可扩展非线性降维方法

• 批准号: 1910539
• 负责人: Jaroslaw Zola
• 金额: $ 49.98万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910539&HistoricalAwards=false
• 关键词: OAC; Core; Small; Scalable; Non

The progress in science and engineering increasingly depends on our ability to analyze massive amounts of observed and simulated data. The vast majority of this data, coming from high-performance high-fidelity simulations, high-resolution sensors, or Internet connected devices, arise from physical processes that, while complex and nonlinear, depend on only few parameters. However, these low-dimension parameters are often hidden in the deluge of high-dimensional data, and are frequently impossible to discover, and thus reason about, by the existing methods. This project will develop new efficient methods to help scientists and engineers, especially in manufacturing and robotics, to simplify complex data such that dynamic processes underlying the data can be better represented, understood and controlled. By leveraging nation?s advanced cyberinfrastructure, these methods will accelerate pace of materials design, reduce the cost and time-to-market of tailored devices, and aid the design, control, and operation of new complex robotic systems. The research outcomes of the project are closely integrated with the educational components, to train the next generation of scientists and engineers on these new technologies, resulting in a skilled and globally competent workforce, especially in the high-priority areas of Artificial Intelligence, Data Science, and Scientific Computing. This project thus promotes advancement of science, welfare and prosperity, as stated by NSF's mission.This multidisciplinary research project aims at developing scalable end-to-end non-linear dimensionality reduction based solutions to accurately learn the dynamic behavior of complex systems. To this end the project introduces new parallel primitives and algorithmic innovations to enable deployment of non-linear spectral dimensionality reduction (NLSDR) and manifold learning methods on the next generation extreme scale computing systems. The project is based on the following key components: i) development of novel locality-aware data distribution and task scheduling strategies for individual NLSDR building blocks taking into account their inter-dependencies when executing in distributed memory environments such as Message Passing Interface and Map/Reduce clusters of multi-core processors, ii) design of new algorithmic strategies to manage data influx while maintaining crucial properties of the sub-manifold characterized by the data, and, iii) development of end-to-end solutions for two transformative example applications pertaining to advanced manufacturing and robotics.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.

科学和工程的进步越来越多地取决于我们分析大量观察到和模拟数据的能力。这些数据的绝大多数来自高性能的高保真模拟，高分辨率传感器或Internet连接的设备，这是由物理过程产生的，尽管复杂和非线性仅取决于很少的参数。但是，这些低维参数通常隐藏在高维数据的泛滥中，并且经常无法通过现有方法发现，因此也是如此。该项目将开发新的有效方法，以帮助科学家和工程师，尤其是在制造和机器人方面，以简化复杂的数据，从而可以更好地表示，理解和控制数据的动态过程。通过利用Nation的高级网络基础设施，这些方法将加速材料设计的速度，降低定制设备的成本和上市时间，并帮助新的复杂机器人系统的设计，控制和操作。该项目的研究成果与教育组成部分紧密融合，以培训下一代的科学家和工程师在这些新技术上，从而培养了熟练且具有全球胜任的劳动力，尤其是在人工智能，数据科学和科学计算的高优势领域。因此，正如NSF的使命所述，该项目促进了科学，福利和繁荣的进步。本多学科研究项目旨在开发可扩展的端到端非线性降低基于基于基于基于的非线性方案的解决方案，以准确了解复杂系统的动态行为。为此，该项目介绍了新的平行原始图和算法创新，以实现下一代极限规模计算系统上的非线性光谱降低降低（NLSDR）和流动学习方法。 The project is based on the following key components: i) development of novel locality-aware data distribution and task scheduling strategies for individual NLSDR building blocks taking into account their inter-dependencies when executing in distributed memory environments such as Message Passing Interface and Map/Reduce clusters of multi-core processors, ii) design of new algorithmic strategies to manage data influx while maintaining crucial properties of the sub-manifold characterized by the数据，以及，iii）开发了与高级制造和机器人技术有关的两个变革性示例应用程序的端到端解决方案。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准来评估值得通过评估来支持的。

项目成果

How important is microstructural feature selection for data-driven structure-property mapping?

微观结构特征选择对于数据驱动的结构-性能映射有多重要？

• DOI: 10.1557/s43579-021-00147-4
• 发表时间: 2022
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Liu, Hao;Yucel, Berkay;Wheeler, Daniel;Ganapathysubramanian, Baskar;Kalidindi, Surya R.;Wodo, Olga
• 通讯作者: Wodo, Olga

Learning Manifolds from Dynamic Process Data

从动态过程数据中学习流形

• DOI: 10.3390/a13020030
• 发表时间: 2020
• 期刊: Algorithms
• 影响因子: 2.3
• 作者: Schoeneman, Frank;Chandola, Varun;Napp, Nils;Wodo, Olga;Zola, Jaroslaw
• 通讯作者: Zola, Jaroslaw

Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark

使用 Apache Spark 解决大型图中的全对最短路径问题

• DOI: 10.1145/3337821.3337852
• 发表时间: 2019
• 期刊: Proceedings of the 48th International Conference on Parallel Processing
• 作者: Schoeneman, Frank;Zola, Jaroslaw
• 通讯作者: Zola, Jaroslaw

Tracking clusters and anomalies in evolving data streams

• DOI: 10.1002/sam.11552
• 发表时间: 2021-10
• 期刊: Statistical Analysis and Data Mining: The ASA Data Science Journal
• 作者: Sreelekha Guggilam;V. Chandola;A. Patra

Efficient Execution of Dynamic Programming Algorithms on Apache Spark

• DOI: 10.1109/cluster49012.2020.00044
• 发表时间: 2020-09
• 期刊: 2020 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: M. Javanmard;Zafar Ahmad;J. Zola;L. Pouchet;R. Chowdhury;R. Harrison