EAGER: SSDIM: Leveraging Point Processes and Mean Field Games Theory for Simulating Data on Interdependent Critical Infrastructures

EAGER：SSDIM：利用点过程和平均场博弈论来模拟相互依赖的关键基础设施上的数据

• 批准号: 1745382
• 负责人: Duen Horng Chau
• 金额: $ 20万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745382&HistoricalAwards=false
• 关键词: EAGER; SSDIM; Leveraging; Point; Processes

This EArly-concept Grant for Exploratory Research (EAGER) project addresses modeling and inference problems in order to improve understanding of interactions and interdependencies within interdependent critical infrastructures (ICIs). The key application areas are financial services, healthcare systems, communication technologies. This work will result in novel machine learning methodologies to generate data on infrastructure interdependencies. Findings will be widely disseminated in scholarly fora, with accompanying efforts in graduate-level training. Data and computer software produced in this project will be made publicly available via online data repositories. This project includes the development of new generative models and algorithms to simulate and synthesize extensive interdependent CI data for comprehensive study. This research focuses on modeling and simulation of interdependent critical infrastructure (ICI) data by leveraging point process models and mean field games (MFG) theory. In particular, multivariate Hawkes processes are used to model interactions and interdependencies of behaviors in a variety of domains. Additionally, an MFG framework is employed to capture the implicit optimization strategies that individuals perform, along with the cost functions that drive those strategies. This work addresses both mechanistic and human aspects of the ICIs, captured in point process models and their evolution. This work advances the theory and computational methods for generative methods and algorithms for quantitative understanding and rigorous analysis of ICIs.

这个探索性研究（EAGER）项目的早期概念资助解决了建模和推理问题，以提高对相互依赖的关键基础设施（ici）中的相互作用和相互依赖性的理解。关键的应用领域是金融服务、医疗保健系统、通信技术。这项工作将产生新的机器学习方法来生成基础设施相互依赖关系的数据。研究结果将在学术论坛上广泛传播，同时进行研究生培训。在这个项目中产生的数据和计算机软件将通过在线数据存储库公开提供。该项目包括开发新的生成模型和算法来模拟和综合广泛的相互依存的CI数据，以进行全面研究。本研究的重点是利用点过程模型和平均场博弈（MFG）理论对相互依赖的关键基础设施（ICI）数据进行建模和仿真。特别地，多元Hawkes过程被用来模拟各种领域中行为的相互作用和相互依赖。此外，MFG框架用于捕获个人执行的隐式优化策略，以及驱动这些策略的成本函数。这项工作解决了在点过程模型及其演变中捕获的ici的机制和人的方面。这项工作为ici的定量理解和严格分析的生成方法和算法提供了理论和计算方法。

项目成果

Learning Conditional Generative Models for Temporal Point Processes

• DOI: 10.1609/aaai.v32i1.12072
• 发表时间: 2018-04
• 作者: Shuai Xiao;Hongteng Xu;Junchi Yan;Mehrdad Farajtabar;Xiaokang Yang;Le Song;H. Zha

Learning to Match via Inverse Optimal Transport

• 发表时间: 2018-02
• 期刊: J. Mach. Learn. Res.
• 作者: Ruilin Li;X. Ye;Haomin Zhou;H. Zha

On Scalable and Efficient Computation of Large Scale Optimal Transport

• 发表时间: 2019-03
• 期刊: ArXiv
• 作者: Yujia Xie;Minshuo Chen;Haoming Jiang;T. Zhao;H. Zha

Network Diffusions via Neural Mean-Field Dynamics

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Shushan He;H. Zha;X. Ye

Acceleration techniques for level bundle methods in weakly smooth convex constrained optimization

弱光滑凸约束优化中水平束方法的加速技术

• DOI: 10.1007/s10589-020-00208-9
• 发表时间: 2020
• 期刊: Computational optimization and applications
• 影响因子: 2.2
• 作者: Chen, Yunmei;Ye, Xiaojing;Zhang, Wei
• 通讯作者: Zhang, Wei