课题基金 / 基金详情

Inference, COmputation and Numerics for Insights into Cities (ICONIC)

Inference, COmputation and Numerics for Insights into Cities (ICONIC)
洞察城市的推理、计算和数值 (ICONIC)
批准号:
EP/P020720/2
负责人:
Mark Girolami
金额:
$297.36万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

项目摘要

项目成果

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中文摘要
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英文摘要
There are many interesting open questions at the interface between applied mathematics, scientific computing and applied statistics.Mathematics is the language of science, we use it to describe the laws of motion that govern natural and technologicalsystems. We use statistics to make sense of data. We develop and test computer algorithms that make these ideas concrete. By bringing these concepts together in a systematic way we can validate and sharpen our hypothesis about the underlying science, and make predictions about future behaviour. This general field of Uncertainty Quantification is a very active area of research, with many challenges; from intellectual questions about how to define and measure uncertainty to very practical issues concerning the need to perform intensive computational experiments as efficiently as possible.ICONIC brings together a team of high profile researchers with the appropriate combination of skills in modeling, numerical analysis, statistics and high performance computing. To give a concrete target for impact, the ICONIC project will focus initially on Uncertainty Quantification for mathematical models relating to crime, security and resilience in urban environments. Then, acknowledging that urban analytics is a very fast-moving field where new technologies and data sources emerge rapidly, and exploiting the flexibility built into an EPSRC programme grant, we will apply the new tools to related city topics concerning human mobility, transport and infrastructure. In this way, the project will enhance the UK's research capabilities in the fast-moving and globally significant Future Cities field.The project will exploit the team's strong existing contacts with Future Cities laboratories around the world, and with nonacademic stakeholders who are keen to exploit the outcomes of the research. As new technologies emerge, and as more people around the world choose to live and work in urban environments, the Future Cities field is generating vast quantities of potentially valuable data. ICONIC will build on the UK's strength in basic mathematical sciences--the cleverness needed to add value to these data sources--in order to produce new algorithms and computational tools. The research will be conducted alongside stakeholders--including law enforcement agencies, technical IT and infrastructure providers, utility companies and policy-makers. These external partners will provide feedback and challenges, and will be ready to extract value from the tools that we develop. We also have an international Advisory Board of committed partners with relevant expertise in academic research, policymaking, law enforcement, business engagement and public outreach. With these structures in place, the research will have a direct impact on the UK economy, as the nation competes for business in the global Future Cities marketplace. Further, by focusing on crime, security and resilience we will directly improve the lives of individual citizens.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
PeriPy - A high performance OpenCL peridynamics package
PeriPy - 高性能 OpenCL 近场动力学软件包
DOI: 10.1016/j.cma.2021.114085
发表时间: 2021
期刊: Computer Methods in Applied Mechanics and Engineering
影响因子: 7.2
作者: [Boys B]
通讯作者: Boys B
Statistical Finite Elements via Langevin Dynamics
Langevin Dynamics 的统计有限元
DOI: 10.48550/arxiv.2110.11131
发表时间: 2021
期刊: arXiv e-prints
影响因子: --
作者: [Akyildiz D]
通讯作者: Akyildiz D
DOI: 10.1177/0272989x211026305
发表时间: 2022-03
期刊: Medical decision making : an international journal of the Society for Medical Decision Making
影响因子: --
作者: [Fang W, Wang Z, Giles MB, Jackson CH, Welton NJ, Andrieu C, Thom H]
通讯作者: Thom H
DOI: 10.1214/18-sts660
发表时间: 2019-02-01
期刊: STATISTICAL SCIENCE
影响因子: 5.7
作者: [Briol, Francois-Xavier, Oates, Chris J., Sejdinovic, Dino]
通讯作者: Sejdinovic, Dino
7
    Semantic Information Pursuit for Multimodal Data Analysis
    • 批准号:
      EP/R018413/2
    • 项目类别:
      Research Grant
    • 资助金额:
      $61.37万
    • 财政年份:
      2019
    • 负责人:
      Mark Girolami
    • 依托单位:
    Semantic Information Pursuit for Multimodal Data Analysis
    • 批准号:
      EP/R018413/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $71.84万
    • 财政年份:
      2018
    • 负责人:
      Mark Girolami
    • 依托单位:
    Inference, COmputation and Numerics for Insights into Cities (ICONIC)
    • 批准号:
      EP/P020720/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $377.68万
    • 财政年份:
      2017
    • 负责人:
      Mark Girolami
    • 依托单位:
    Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications
    • 批准号:
      EP/J016934/3
    • 项目类别:
      Fellowship
    • 资助金额:
      $30.03万
    • 财政年份:
      2016
    • 负责人:
      Mark Girolami
    • 依托单位:
    国内基金
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    基于分位数g-computation的多污染物联合空气质量健康指数构建及预测效果评价
    • 批准号:
      --
    • 项目类别:
      青年科学基金项目
    • 资助金额:
      30万元
    • 批准年份:
      2022
    • 负责人:
      李嘉琛
    • 依托单位:
    基于g-computation控制纵向数据未测混杂因素的因果推断模型构建及应用研究
    • 批准号:
      81903416
    • 项目类别:
      青年科学基金项目
    • 资助金额:
      19.0万元
    • 批准年份:
      2019
    • 负责人:
      陈永杰
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