SONNETS: Scalability Oriented Novel Network of Event Triggered Systems

SONNETS:面向可扩展性的事件触发系统新型网络

基本信息

  • 批准号:
    EP/X036006/1
  • 负责人:
  • 金额:
    $ 824.1万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2024
  • 资助国家:
    英国
  • 起止时间:
    2024 至 无数据
  • 项目状态:
    未结题

项目摘要

SONNETS - Scalability Oriented Novel Networks of Event Triggered Systems - takes a clean-slate approach to next-generation computer modelling and artificial intelligence. To drive this we have an over-arching research goal that is both nationally important and challenging: real-time modelling of UK financial risk.It is easy to identify underlying risks after they cause a financial crisis. With hindsight, the 2008 financial crash was caused by too many banks buying too many risky mortgages. Whilst the crisis was unfolding it was all new information: no-one realised how many banks owned the risky mortgages. Then it was assumed that mortgage defaults were unlikely. Finally, it was assumed that losses in a few banks would not affect the national economy. The problem was a lack of visibility and understanding of the national picture: each bank appeared to have a manageable risk level, but most banks in the UK were exposed to the same underlying risk factor, so once mortgages started defaulting most banks started losing money and a perfect financial storm developed. What we needed then, and still do now, is national-level risk modelling that can consider risk across banks as it occurs.Modelling risk for one bank is a difficult problem, and modelling the entire UK is much harder. Banks have complex constantly changing portfolios, so building a picture of "who owns what" means tracking millions of trades per day. Even if we have that picture we still need to somehow assess risk, but that requires anticipating the future: we must pre-emptively identify potential scenarios, then estimate how much is lost in each scenario. Currently regulators use "stress tests" to identify national risk - they define a possible challenging economic scenario, then ask all the banks to estimate how much they might lose. However, this is both slow - the process takes months - and limited - they only explore one very severe scenario, which probably isn't the one that causes the problem.SONNETS will create a system that performs national-level risk analysis in real-time, by building a "digital twin" of the UK's financial system and using it to continually generate plausible future scenarios and assess their risk. We then use artificial intelligence to learn what risky scenarios look like. This gives regulators completely new tools:- A day-by-day view of the current national-risk of the UK, rather than waiting months for stress tests;- The ability to look forwards to identify and mitigate previously unknown risks as they develop, rather than waiting for a financial crisis to reveal them.We tackle this problem by addressing challenges in three main areas:- Computing: new paradigms for creating and running programs, exploiting multiple types of computer hardware distributed across the cloud;- Artificial Intelligence: methods for continual learning that can be split into multiple pieces, so that learning processes can be moved closer to the data they are learning from;- Modelling: theory and tools for automatic scenario generation, plus the ability to assess risk over large-scale models of the UK's financial institutions.These three areas are tightly linked, with the new computing paradigms supporting execution of the new AI and modelling in the cloud, and a synergistic relationship between the modelling of the system and learning about the model.Underpinning these three areas is the idea of event-triggered computing, where programs are split up into small fragments which send messages to each other. Using this event-triggered approach we can scale the risk analysis system up to support national-level risk analysis. It will constantly assess how risky the UK currently is, while trying to anticipate what scenarios might lead to financial crises in the future.SONNETS will provide a powerful tool to detect and mitigate financial risk as it is building up, rather than trying to react to a financial crisis once it happens.
SONNETS——面向可扩展性的事件触发系统新网络——采用了一种全新的方法来实现下一代计算机建模和人工智能。为了推动这一点,我们有一个总体的研究目标,既具有全国重要性又具有挑战性:英国金融风险的实时建模。在潜在风险引发金融危机之后,识别它们是很容易的。事后看来,2008年的金融危机是由太多银行购买了太多风险抵押贷款造成的。在危机爆发之际,所有的信息都是新的:没有人意识到有多少银行拥有高风险抵押贷款。当时人们认为抵押贷款违约不太可能发生。最后,假定少数银行的损失不会影响国民经济。问题在于缺乏可视性和对全国情况的了解:每家银行似乎都有一个可控的风险水平,但英国的大多数银行都面临着同样的潜在风险因素,因此,一旦抵押贷款开始违约,大多数银行就开始亏损,一场完美的金融风暴就形成了。我们当时需要(现在仍然需要)的是国家层面的风险建模,能够在风险发生时考虑各银行之间的风险。对一家银行进行风险建模是一个难题,对整个英国进行风险建模则困难得多。银行拥有复杂的、不断变化的投资组合,因此建立“谁拥有什么”的图景意味着每天要追踪数百万笔交易。即使我们有这样的设想,我们仍然需要以某种方式评估风险,但这需要预测未来:我们必须先发制人地识别潜在的情景,然后估计每种情景的损失。目前,监管机构使用“压力测试”来识别国家风险——他们定义一种可能具有挑战性的经济情景,然后要求所有银行估计它们可能会损失多少。然而,这是缓慢的-这个过程需要几个月-和有限的-他们只探索一个非常严重的情况,这可能不是导致问题的那个。SONNETS将创建一个系统,通过建立英国金融体系的“数字双胞胎”,并利用它不断生成可信的未来情景,并评估其风险,从而实时执行国家级风险分析。然后,我们使用人工智能来了解有风险的场景是什么样的。这为监管机构提供了全新的工具:-对英国当前国家风险的逐日观察,而不是等待数月的压力测试;-前瞻性的能力,在风险发展时识别和减轻以前未知的风险,而不是等待金融危机来揭示它们。我们通过解决三个主要领域的挑战来解决这个问题:-计算:创建和运行程序的新范例,利用分布在云上的多种类型的计算机硬件;-人工智能:持续学习的方法,可以分成多个部分,这样学习过程可以更接近他们正在学习的数据;-建模:用于自动场景生成的理论和工具,以及评估英国金融机构大规模模型风险的能力。这三个领域紧密相连,新的计算范式支持在云中执行新的人工智能和建模,并且在系统建模和学习模型之间存在协同关系。支撑这三个领域的是事件触发计算的思想,在这种思想中,程序被分割成小的片段,彼此发送消息。使用这种事件触发的方法,我们可以扩大风险分析系统的规模,以支持国家层面的风险分析。它将不断评估英国目前的风险,同时试图预测未来可能导致金融危机的情况。十四行诗将提供一个强大的工具来检测和减轻金融风险,因为它正在积累,而不是试图应对金融危机一旦发生。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

David Thomas其他文献

The Reconfiguration of the Archive as Data to Be Mined
将档案重新配置为待挖掘的数据
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michael Moss;David Thomas;Tim Gollins
  • 通讯作者:
    Tim Gollins
Using MET/min/wk(-1) to prescribe aerobic exercise to healthy young females
使用 MET/min/wk(-1) 为健康年轻女性规定有氧运动
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    David Thomas
  • 通讯作者:
    David Thomas
Using body mapping as part of the risk assessment process – a case study
使用人体测绘作为风险评估过程的一部分——案例研究
DNA entropy reveals a significant difference in complexity between housekeeping and tissue specific gene promoters
DNA 熵揭示了管家基因启动子和组织特异性基因启动子之间复杂性的显着差异
  • DOI:
    10.1016/j.compbiolchem.2015.05.001
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    David Thomas;C. Finan;M. Newport;Susan Jones
  • 通讯作者:
    Susan Jones
A further perspective on parental reaction to handicap
关于父母对残障反应的进一步看法
  • DOI:
    10.1080/0156655860330207
  • 发表时间:
    1986
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    R. Burden;David Thomas
  • 通讯作者:
    David Thomas

David Thomas的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('David Thomas', 18)}}的其他基金

Alluvial landscape evolution in response to deglaciation. A case study from the Thompson River, south-central British Columbia.
冲积地貌演化响应冰消作用。
  • 批准号:
    NE/X007081/1
  • 财政年份:
    2022
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Christian-Muslim Relations, a Bibliographical History 1800-1914 (CMR 1914)
基督教与穆斯林关系,1800-1914 年书目史 (CMR 1914)
  • 批准号:
    AH/N00809X/1
  • 财政年份:
    2016
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Christian-Muslim Relations 1500-1900 (CMR1900)
基督教与穆斯林关系 1500-1900 (CMR1900)
  • 批准号:
    AH/J003875/1
  • 财政年份:
    2012
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Simplified models of emissions from electronic systems based on near field measurements
基于近场测量的电子系统辐射简化模型
  • 批准号:
    EP/H051384/1
  • 财政年份:
    2011
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Integrated Macro-models of Networks and Fields for the Simulation of Complex Systems
用于复杂系统仿真的网络和场综合宏观模型
  • 批准号:
    EP/G009201/1
  • 财政年份:
    2008
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
REU Site: Internship in Supercomputing for Physical Sciences
REU 网站:物理科学超级计算实习
  • 批准号:
    0755186
  • 财政年份:
    2008
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Continuing Grant
Improving Chronic Wound healing with Intelligent Dressings
使用智能敷料改善慢性伤口愈合
  • 批准号:
    EP/D505445/1
  • 财政年份:
    2006
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Electromagnetic characterisation of printed circuit boards
印刷电路板的电磁特性
  • 批准号:
    EP/D048540/1
  • 财政年份:
    2006
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
The bibliographical history of Christian-Muslim Relations 1
基督教与穆斯林关系的书目史 1
  • 批准号:
    119223/1
  • 财政年份:
    2006
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
REU Site: Research Experiences for Undergraduates Internship in Supercomputing for Physical Science and Mathematics
REU网站:物理科学和数学超级计算本科生实习研究经验
  • 批准号:
    0452204
  • 财政年份:
    2005
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Continuing Grant

相似海外基金

Multiscale Approaches And Scalability Within Climate Change-heritage Risk Assessments
气候变化遗产风险评估中的多尺度方法和可扩展性
  • 批准号:
    AH/Z000084/1
  • 财政年份:
    2024
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Automatic battery swapping cabinet development for scalability of e-mobility in Uganda
自动电池交换柜开发,以提高乌干达电动汽车的可扩展性
  • 批准号:
    10080435
  • 财政年份:
    2024
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Collaborative R&D
Applicability and scalability of a sustainable re-construction framework for seismic-prone heritage areas of Gujarat, India.
印度古吉拉特邦地震多发遗产地区可持续重建框架的适用性和可扩展性。
  • 批准号:
    AH/X006832/2
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Maximizing the Scalability of the Chronic Disease Self-Management Program (CDSMP) Among Older Adults in State Correctional Settings
最大限度地提高州惩教机构中老年人慢性病自我管理计划 (CDSMP) 的可扩展性
  • 批准号:
    10654994
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
Research on Scalability of Permissioned Blockchain for IoT
物联网许可区块链可扩展性研究
  • 批准号:
    23H03377
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Printing Perovskite Solar Cells: Reducing Toxicity and Improving Scalability
打印钙钛矿太阳能电池:降低毒性并提高可扩展性
  • 批准号:
    EP/X03660X/1
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
SBIR Phase I: Engineering Scalability of Durable Low-Noble-Metal-Content Fuel Cell Catalysts
SBIR 第一阶段:耐用低贵金属含量燃料电池催化剂的工程可扩展性
  • 批准号:
    2151576
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Standard Grant
Project CLASSIFIES: Common Language Assessment in Studying Statistics with Instructional Feedback and Increased Enrollment Scalability
项目分类:具有教学反馈和提高招生可扩展性的统计研究中的通用语言评估
  • 批准号:
    2236150
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Standard Grant
Applicability and scalability of a sustainable re-construction framework for seismic-prone heritage areas of Gujarat, India.
印度古吉拉特邦地震多发遗产地区可持续重建框架的适用性和可扩展性。
  • 批准号:
    AH/X006832/1
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Research Grant
Feasibility study into methods for improving the scalability of MICP (Microbially Induced Calcite Precipitation) for the production of Low carbon Precast Tiles and internal cladding systems
提高 MICP(微生物诱导方解石沉淀)生产低碳预制砖和内部覆层系统可扩展性的方法的可行性研究
  • 批准号:
    10067974
  • 财政年份:
    2023
  • 资助金额:
    $ 824.1万
  • 项目类别:
    Collaborative R&D
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了