Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
基本信息
- 批准号:RGPIN-2017-06375
- 负责人:
- 金额:$ 2.04万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The proposed statistical framework intends to provide a rational approach to reconciling running computational simulations with measurement data in uncertain and rapidly changing environments for accurate and realistic numerical predictions for informed and optimal decision-making. The long-term objective of the proposed research is to develope uncertainty quantification, data assimilation and model selection algorithms which can exploit the extreme-scale parallelism required to effectively leverage petascale and future exascale systems with millions of cores and accelerators (e.g. graphics processing units and coprocessors). The proposed algorithms will be applied to tackle computational stochastic mechanics problems related to (a) real-time nonlinear aeroelastic computations using streaming wind-tunnel data; (b) uncertainty quantification in middle frequency structural dynamics; and (c) nonlinear (elasto-plastic) seismic wave propagations through random geological media. The novelties of the proposed research are: (1) the mathematical formulation and distributed implemention of MH-MCMC algorithms for Bayesian parameter estimation and model selection in conjunction with its experimental validation for nonlinear aeroelastic computation using streaming wind-tunnel data; and (2) the development of an intrusive polynomial chaos-based multi-level domain decomposition algorithm for SPDES for ultra-scale computations in petascale/future exascale systems, along with its applications to the middle frequency structural dynamics and elasto-plastic seismic wave propagation in random soil strata. The uncertainty quantification and data assimilation in computational models will boost public confidence and regulatory acceptance in order to assess the safety and reliability of engineering systems for aeroelastic performance of long-span bridges, air vehicles, seismic risk assessment of dams and nuclear reactors and, detection of military activities from seismic signatures. The proposed Bayesian estimation framework for computional modeling can bridge the gap between experimentalists and numerical modelers in various disciplines in science and engineering. Capitalizing on the investments in large-scale national computing facilities, the proposed research initiatives will offer technological leadership to Canada including the development of a highly skilled workforce capable of high performance computing, uncertainty quantification and statistical inference relevant to computational and data science applications.
拟议的统计框架旨在提供一种合理的方法来协调运行的计算模拟与测量数据在不确定和快速变化的环境中准确和现实的数值预测知情和最佳决策。拟议研究的长期目标是开发不确定性量化,数据同化和模型选择算法,这些算法可以利用有效利用具有数百万个核心和加速器(例如图形处理单元和协处理器)的千万亿次和未来亿亿次系统所需的极端规模并行性。所提出的算法将应用于解决与以下方面有关的计算随机力学问题:(a)使用流式风洞数据的实时非线性气动弹性计算;(B)中频结构动力学中的不确定性量化;以及(c)通过随机地质介质的非线性(弹塑性)地震波传播。本文的创新点在于:(1)提出了用于贝叶斯参数估计和模型选择的MH-MCMC算法的数学公式和分布式实现方法,并结合流场风洞非线性气动弹性计算进行了实验验证;(2)提出了一种基于多项式混沌的SPDES多层区域分解算法,用于千万亿次/千万亿次的超大规模计算。未来的十亿系统,沿着其应用的中频结构动力学和弹塑性地震波在随机土层中的传播。计算模型中的不确定性量化和数据同化将提高公众的信心和监管接受度,以评估工程系统的安全性和可靠性,用于大跨度桥梁的气动弹性性能,飞行器,大坝和核反应堆的地震风险评估,以及从地震特征中检测军事活动。提出的贝叶斯估计框架计算建模可以弥合差距之间的差距,实验和数值模拟在科学和工程的各个学科。利用对大规模国家计算设施的投资,拟议的研究举措将为加拿大提供技术领导地位,包括培养一支能够进行高性能计算、不确定性量化和与计算和数据科学应用相关的统计推断的高技能劳动力。
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 }}
Sarkar, Abhijit其他文献
Elevated ozone and two modern wheat cultivars: An assessment of dose dependent sensitivity with respect to growth, reproductive and yield parameters
- DOI:
10.1016/j.envexpbot.2010.04.016 - 发表时间:
2010-12-01 - 期刊:
- 影响因子:5.7
- 作者:
Sarkar, Abhijit;Agrawal, S. B. - 通讯作者:
Agrawal, S. B.
Scalable domain decomposition solvers for stochastic PDEs in high performance computing
- DOI:
10.1016/j.cma.2017.09.006 - 发表时间:
2018-06-15 - 期刊:
- 影响因子:7.2
- 作者:
Desai, Ajit;Khalil, Mohammad;Sarkar, Abhijit - 通讯作者:
Sarkar, Abhijit
Protective effect of arjunolic acid against atorvastatin induced hepatic and renal pathophysiology via MAPK, mitochondria and ER dependent pathways
- DOI:
10.1016/j.biochi.2015.02.016 - 发表时间:
2015-05-01 - 期刊:
- 影响因子:3.9
- 作者:
Pal, Sankhadeep;Sarkar, Abhijit;Sil, Parames C. - 通讯作者:
Sil, Parames C.
Influence of North Atlantic Oscillation on Indian Summer Monsoon Rainfall in Relation to Quasi-Binneal Oscillation
- DOI:
10.1007/s00024-016-1306-z - 发表时间:
2016-08-01 - 期刊:
- 影响因子:2
- 作者:
Bhatla, R.;Singh, A. K.;Sarkar, Abhijit - 通讯作者:
Sarkar, Abhijit
Unravelling the Complexity of Irregular Shiftwork, Fatigue and Sleep Health for Commercial Drivers and the Associated Implications for Roadway Safety.
- DOI:
10.3390/ijerph192214780 - 发表时间:
2022-11-10 - 期刊:
- 影响因子:0
- 作者:
Mabry, Jessica Erin;Camden, Matthew;Miller, Andrew;Sarkar, Abhijit;Manke, Aditi;Ridgeway, Christiana;Iridiastadi, Hardianto;Crowder, Tarah;Islam, Mouyid;Soccolich, Susan;Hanowski, Richard J. - 通讯作者:
Hanowski, Richard J.
Sarkar, Abhijit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('Sarkar, Abhijit', 18)}}的其他基金
Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
- 批准号:
RGPIN-2017-06375 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
- 批准号:
RGPIN-2017-06375 - 财政年份:2020
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
- 批准号:
RGPIN-2017-06375 - 财政年份:2019
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
- 批准号:
RGPIN-2017-06375 - 财政年份:2018
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Scalable Algorithms for Uncertainty Quantification and Bayesian Inference with Applications to Computational Mechanics
不确定性量化和贝叶斯推理的可扩展算法及其在计算力学中的应用
- 批准号:
RGPIN-2017-06375 - 财政年份:2017
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
A scalable uncertainty quantification and data assimilation framework for tracking stochastic fluid interfaces: application to civil and environmental engineering
用于跟踪随机流体界面的可扩展不确定性量化和数据同化框架:在土木和环境工程中的应用
- 批准号:
312528-2010 - 财政年份:2016
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
A scalable uncertainty quantification and data assimilation framework for tracking stochastic fluid interfaces: application to civil and environmental engineering
用于跟踪随机流体界面的可扩展不确定性量化和数据同化框架:在土木和环境工程中的应用
- 批准号:
312528-2010 - 财政年份:2013
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Uncertainty Quantification with High Performance Computing Applications
高性能计算应用程序的不确定性量化
- 批准号:
1000209063-2008 - 财政年份:2013
- 资助金额:
$ 2.04万 - 项目类别:
Canada Research Chairs
Uncertainty Quantification with High Performance Computing Applications
高性能计算应用程序的不确定性量化
- 批准号:
1000209063-2008 - 财政年份:2012
- 资助金额:
$ 2.04万 - 项目类别:
Canada Research Chairs
A scalable uncertainty quantification and data assimilation framework for tracking stochastic fluid interfaces: application to civil and environmental engineering
用于跟踪随机流体界面的可扩展不确定性量化和数据同化框架:在土木和环境工程中的应用
- 批准号:
312528-2010 - 财政年份:2012
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
相似海外基金
CAREER: Robust Reinforcement Learning Under Model Uncertainty: Algorithms and Fundamental Limits
职业:模型不确定性下的鲁棒强化学习:算法和基本限制
- 批准号:
2337375 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
Collaborative Research: AF: Medium: Algorithms Meet Machine Learning: Mitigating Uncertainty in Optimization
协作研究:AF:媒介:算法遇见机器学习:减轻优化中的不确定性
- 批准号:
2422926 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
CAREER: Scalable and Robust Uncertainty Quantification using Subsampling Markov Chain Monte Carlo Algorithms
职业:使用子采样马尔可夫链蒙特卡罗算法进行可扩展且稳健的不确定性量化
- 批准号:
2340586 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
UQ4FM: Uncertainty quantification algorithms for flood modelling
UQ4FM:洪水建模的不确定性量化算法
- 批准号:
EP/X040941/1 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Research Grant
CAREER: AF: Algorithms for Facility Location Problems with Uncertainty
职业:AF:具有不确定性的设施位置问题的算法
- 批准号:
2339371 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
Integrated Framework for Cooperative 3D Printing: Uncertainty Quantification, Decision Models, and Algorithms
协作 3D 打印的集成框架:不确定性量化、决策模型和算法
- 批准号:
2329739 - 财政年份:2024
- 资助金额:
$ 2.04万 - 项目类别:
Standard Grant
Co-Design of Neural Operators and Stochastic Optimization Algorithms for Learning Surrogates for PDE-Constrained Optimization Under Uncertainty
不确定性下偏微分方程约束优化学习代理的神经算子和随机优化算法的协同设计
- 批准号:
2324643 - 财政年份:2023
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
CAREER: Efficient Uncertainty Quantification in Turbulent Combustion Simulations: Theory, Algorithms, and Computations
职业:湍流燃烧模拟中的高效不确定性量化:理论、算法和计算
- 批准号:
2143625 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant
Structure-Preserving Algorithms for Hyperbolic Balance Laws with Uncertainty
不确定性双曲平衡定律的结构保持算法
- 批准号:
2207207 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Standard Grant
CAREER: Uncertainty Quantification for Quantum Computing Algorithms
职业:量子计算算法的不确定性量化
- 批准号:
2143915 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Continuing Grant