ATD: Resilient Dynamic Autoencoders for Modeling and Predicting Earthquake Threats
ATD:用于建模和预测地震威胁的弹性动态自动编码器
基本信息
- 批准号:2319621
- 负责人:
- 金额:$ 22万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Large earthquakes generate strong ground motions and tsunamis that may lead to a significant number of casualties and cause severe impacts on social resilience in seismically active regions including the West Coast of the United States. Early warning systems have been developed to mitigate immediate threats by detecting first-arriving ground motions near an earthquake epicenter and forecasting the intensity and timing of strong destructive ground motions. To further improve the efficacy and accuracy of these systems, deep learning methods have strong potential, but it is crucial to significantly extend the forecast horizons of existing models. The highly heterogeneous spatiotemporal nature of the seismic wave propagation poses a fundamental challenge to machine learning. This project aims to address these challenges by developing resilient and reliable deep learning methods for forecasting noisy and complex spatiotemporal ground motion data. The resulting methods and open-source software tools will have applications beyond seismology, benefiting diverse scientific and engineering domains including earth, atmospheric, and climate sciences. Furthermore, the project provides research training opportunities for undergraduate and graduate students.The project will primarily focus on advancing deep learning for spatiotemporal data processing. It will develop practical theory for (i) learning continuous dynamics, (ii) modeling multiscale structures jointly in space and time, and (iii) improving robustness of neural networks to natural perturbations in the input data. Computational deliverables will be neural network architectures for learning robust latent space embeddings and improved forecasting, which will account for uncertainties in ground motion data caused by sensor noise and scarcity in seismic recordings. Both observed and simulated ground motion data will be used for demonstrating the advantages of the proposed methods. The technical approach combines ideas from fields such as dynamical systems theory, seismology, and deep learning. By viewing spatiotemporal data processing and robustness through the lens of dynamical systems theory, the project aims to establish a principled framework that will significantly impact a broad range of scientific problems.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.
大地震产生强烈的地面运动和海啸,可能导致大量人员伤亡,并对包括美国西海岸在内的地震活跃地区的社会复原力造成严重影响。早期预警系统的发展是为了通过探测地震震中附近最先到达的地面运动和预测强烈破坏性地面运动的强度和时间来减轻眼前的威胁。为了进一步提高这些系统的有效性和准确性,深度学习方法具有强大的潜力,但重要的是要显着扩展现有模型的预测范围。地震波传播的高度异构时空性质对机器学习提出了根本性的挑战。该项目旨在通过开发弹性和可靠的深度学习方法来预测嘈杂和复杂的时空地面运动数据来应对这些挑战。由此产生的方法和开源软件工具将具有超越地震学的应用,使包括地球,大气和气候科学在内的各种科学和工程领域受益。此外,该项目还为本科生和研究生提供研究培训机会。该项目将主要关注推进时空数据处理的深度学习。它将发展实用理论(i)学习连续动态,(ii)在空间和时间上联合建模多尺度结构,以及(iii)提高神经网络对输入数据中自然扰动的鲁棒性。计算交付成果将是神经网络架构,用于学习强大的潜在空间嵌入和改进的预测,这将考虑到传感器噪声和地震记录稀缺造成的地面运动数据的不确定性。观测和模拟的地面运动数据将被用来证明所提出的方法的优点。该技术方法结合了动力系统理论、地震学和深度学习等领域的思想。通过观察时空数据处理和鲁棒性通过动力系统理论的透镜,该项目旨在建立一个原则性的框架,将显着影响广泛的科学问题。该奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
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 }}
Nils Benjamin Erichson其他文献
Nils Benjamin Erichson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 }}
相似海外基金
Fully Decentralized (Attack-)Resilient Dynamic Low-Rank Matrix Learning
完全去中心化(攻击)弹性动态低秩矩阵学习
- 批准号:
2213069 - 财政年份:2022
- 资助金额:
$ 22万 - 项目类别:
Standard Grant
Dynamic Network Traffic Identification with Scalable and Resilient Machine Learning
通过可扩展且有弹性的机器学习进行动态网络流量识别
- 批准号:
576922-2022 - 财政年份:2022
- 资助金额:
$ 22万 - 项目类别:
Alliance Grants
CAREER: Morphological Computation for Resilient Dynamic Locomotion of Compliant Legged Robots with Application to Precision Agriculture
职业:顺应腿式机器人弹性动态运动的形态计算及其在精准农业中的应用
- 批准号:
2046270 - 财政年份:2021
- 资助金额:
$ 22万 - 项目类别:
Standard Grant
Elastic Manufacturing systems - a platform for dynamic, resilient and cost-effective manufacturing services
弹性制造系统 - 动态、弹性和经济高效的制造服务平台
- 批准号:
EP/T024429/1 - 财政年份:2020
- 资助金额:
$ 22万 - 项目类别:
Research Grant
A DYNAMIC APPROACH TO INVESTIGATE RESILIENT SUPPLY CHAIN-TRANSPORT SUPERNETWORK
研究弹性供应链运输超级网络的动态方法
- 批准号:
18K04389 - 财政年份:2018
- 资助金额:
$ 22万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Towards More Resilient Transit Networks through Monitoring, Targeted Passenger Information and Dynamic Fleet Scheduling
通过监控、有针对性的乘客信息和动态车队调度,打造更具弹性的交通网络
- 批准号:
RGPIN-2014-04911 - 财政年份:2018
- 资助金额:
$ 22万 - 项目类别:
Discovery Grants Program - Individual
Resilient linear infrastructure through dynamic distributed fibre optic strain sensing
通过动态分布式光纤应变传感实现弹性线性基础设施
- 批准号:
RTI-2018-00428 - 财政年份:2017
- 资助金额:
$ 22万 - 项目类别:
Research Tools and Instruments
Towards More Resilient Transit Networks through Monitoring, Targeted Passenger Information and Dynamic Fleet Scheduling
通过监控、有针对性的乘客信息和动态车队调度,打造更具弹性的交通网络
- 批准号:
RGPIN-2014-04911 - 财政年份:2017
- 资助金额:
$ 22万 - 项目类别:
Discovery Grants Program - Individual
Collaborative Research: CICI: Secure and Resilient Architecture: Creating Dynamic Superfacilities the SAFE Way
合作研究:CICI:安全和弹性架构:以安全方式创建动态超级设施
- 批准号:
1642142 - 财政年份:2016
- 资助金额:
$ 22万 - 项目类别:
Standard Grant