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依赖于我们分析大量观测和模拟数据的能力。这些数据中的绝大多数来自高性能的高保真模拟、高分辨率传感器或连接互联网的设备，这些数据来自物理过程，尽管复杂和非线性，但只依赖于少数参数。然而，这些低维参数往往隐藏在高维数据的洪流中，通常不可能被现有方法发现，从而无法推理。该项目将开发新的有效方法来帮助科学家和工程师，特别是制造业和机器人领域的科学家和工程师，简化复杂的数据，以便更好地表示、理解和控制数据背后的动态过程。通过利用国家-S先进的网络基础设施，这些方法将加快材料设计的步伐，降低定制设备的成本和上市时间，并帮助设计、控制和操作新的复杂机器人系统。该项目的研究成果与教育部分紧密结合，以培训下一代科学家和工程师掌握这些新技术，从而培养出一支熟练的、全球胜任的劳动力队伍，特别是在人工智能、数据科学和科学计算等高度优先的领域。这个多学科研究项目的目的是开发可扩展的端到端非线性降维解决方案，以准确学习复杂系统的动态行为。为此，该项目引入了新的并行基元和算法创新，以使能够在下一代极端规模计算系统上部署非线性谱降维(NLSDR)和多种学习方法。该项目基于以下关键组件：i)为各个NLSDR构建块开发新的局部性感知数据分发和任务调度策略，考虑到它们在分布式存储环境中执行时的相互依赖关系，例如多核处理器的消息传递接口和映射/还原集群；ii)设计新的算法策略来管理数据流入，同时保持以数据为特征的子流形的关键属性；以及，Iii)为两个与先进制造和机器人相关的变革性示例应用程序开发端到端解决方案。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。

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

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

Graph-based Strategy for Establishing Morphology Similarity

基于图的建立形态相似性的策略

• DOI: 10.1145/3468791.3468819
• 发表时间: 2021
• 期刊: International Conference on Scientific and Statistical Database Management (SSDBM
• 作者: Juneja, Namit;Zola, Jaroslaw;Chandola, Varun;Wodo, Olga
• 通讯作者: Wodo, Olga