Overcoming the Bottlenecks in Polynomial Chaos: Algorithms and Applications to Systems Biology and Fluid Mechanics
克服多项式混沌的瓶颈:系统生物学和流体力学的算法和应用
基本信息
- 批准号:0915077
- 负责人:
- 金额:$ 20万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-10-01 至 2013-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The PI proposes to develop new effectrive methods for solving stochastic partial differential equations (SPDEs). In particular, the PI will address two outstanding issues in polynomial chaos (PC) methods for modeling uncertainty in computer simulations of physical and biological systems. The first one is related to treating effectively many stochastic dimensions while the second one is related to modeling accurately white noise. Such problems arise in applications with small relative correlation length or large number of independent random parameters. The two approaches are complementary to each other as problems with very small correlation length can be effectively modeled by white noise processes.The new ideas are the use of ANOVA decomposition and the introduction of proper weighted Wiener chaosspaces and stochastic convolution products. ANOVA provides a hierarchical functional decomposition that exploits the effective dimensionality of the system. This type of dimension-wise decomposition can effectively break the curse of dimensionality in certain approximation problems in which the effective dimensionality is much lower than the nominal dimensionality. In preliminary work, the PI has demonstrated the effectiveness of the new approach in approximating efficiently problems with more than 500 dimensions.The proposed work will have significant and broad impact as it will set rigorous foundations in uncertainty quantification, data assimilation and sensitivity analysis for many physical and biological systems. For example, in computational fluid dynamics, it will establish a robust and efficient framework to endow simulations with a composite error bar that goes beyond numerical accuracy and includes uncertainties in operating conditions, the physical parameters, and the domain.The proposed work is transformative as it will make stochastic simulations the standard rather than the exception. It will also affect fundamentally the way new experiments are designed and the type of questions that can be addressed, while the interaction between simulation and experiment will become more meaningful and more dynamic.The PI plans to incorporate these new ideas in engineering and applied mathematics courses at Brown. Sponsored graduate and undergraduate students will be involved in this research and will interact withall senior personnel that includes several international visitors. The PI will work closely with undergraduate students who are involved with outreach activities through two very effective organizations at Brown that target women in science and engineering and also middle school students. He also plans outreach activities for inner-city high schools by developing along with the teachers computer-based interactive math learning strategies. Preliminary results working with the MET school have been very encouraging, and the PI plans to expand this activity nationwide.
PI提出了一种新的有效的求解随机偏微分方程的方法。特别是,PI将解决多项式混沌(PC)方法中用于对物理和生物系统的计算机模拟中的不确定性进行建模的两个突出问题。第一个问题与有效处理多个随机维度有关,第二个问题与准确建模白噪声有关。这类问题出现在相对相关长度较小或具有大量独立随机参数的应用中。这两种方法是相辅相成的,因为关联长度很小的问题可以用白噪声过程有效地建模。新的思想是使用ANOVA分解,并引入适当的加权Wiener混沌空间和随机卷积。ANOVA提供了一种利用系统有效维度的层级功能分解。在有效维度远低于名义维度的某些逼近问题中,这种逐维分解可以有效地打破维度灾难。在初步工作中,PI已经证明了新方法的有效性,可以有效地逼近500维以上的问题。拟议的工作将产生重大而广泛的影响,因为它将为许多物理和生物系统的不确定性量化、数据同化和灵敏度分析奠定坚实的基础。例如,在计算流体力学中,它将建立一个稳健而有效的框架,为模拟提供一个复合误差条,它超出了数值精度,包括操作条件、物理参数和域的不确定性。拟议的工作具有变革性,因为它将使随机模拟成为标准而不是例外。它还将从根本上影响新实验的设计方式和可以解决的问题类型,而模拟和实验之间的交互将变得更有意义和更动态。PI计划将这些新想法纳入布朗大学的工程和应用数学课程。受资助的研究生和本科生将参与这项研究,并将与包括几名国际访问者在内的所有高级人员进行互动。PI将通过布朗大学两个非常有效的组织,以科学和工程领域的女性以及中学生为目标,与参与外展活动的本科生密切合作。他还计划为市中心的高中开展外展活动,与教师一起开发基于计算机的交互式数学学习策略。与大都会学校合作的初步结果非常令人鼓舞,国际和平协会计划在全国范围内推广这一活动。
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)
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George Karniadakis其他文献
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
- DOI:
10.1007/s00466-019-01747-7 - 发表时间:
2019-06-28 - 期刊:
- 影响因子:3.800
- 作者:
Wing Kam Liu;George Karniadakis;Shaoqiang Tang;Julien Yvonnet - 通讯作者:
Julien Yvonnet
Physics-Informed Learning Machines for Partial Differential Equations: Gaussian Processes Versus Neural Networks
用于偏微分方程的物理学习机:高斯过程与神经网络
- DOI:
10.1007/978-3-030-44992-6_14 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Guofei Pang;George Karniadakis - 通讯作者:
George Karniadakis
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
- DOI:
10.1016/j.cma.2023.116681 - 发表时间:
2024-02-15 - 期刊:
- 影响因子:
- 作者:
Ehsan Haghighat;Umair bin Waheed;George Karniadakis - 通讯作者:
George Karniadakis
Simulating and visualizing the human arterial system on the TeraGrid
- DOI:
10.1016/j.future.2006.03.019 - 发表时间:
2006-10-01 - 期刊:
- 影响因子:
- 作者:
Suchuan Dong;Joseph Insley;Nicholas T. Karonis;Michael E. Papka;Justin Binns;George Karniadakis - 通讯作者:
George Karniadakis
CMINNs: Compartment model informed neural networks — Unlocking drug dynamics
- DOI:
10.1016/j.compbiomed.2024.109392 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:
- 作者:
Nazanin Ahmadi Daryakenari;Shupeng Wang;George Karniadakis - 通讯作者:
George Karniadakis
George Karniadakis的其他文献
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{{ truncateString('George Karniadakis', 18)}}的其他基金
Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior
合作研究:AMPS:通过机器学习进行多保真度建模,实时预测电力系统行为
- 批准号:
1736088 - 财政年份:2017
- 资助金额:
$ 20万 - 项目类别:
Continuing Grant
MANNA 2017: Modeling, Analysis, and Numerics for Nonlocal Applications
MANNA 2017:非局部应用的建模、分析和数值
- 批准号:
1747867 - 财政年份:2017
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
New evolution equations of the joint response-excitation PDF for stochastic modeling: Theory and numerical methods
用于随机建模的联合响应激励 PDF 的新演化方程:理论和数值方法
- 批准号:
1216437 - 财政年份:2012
- 资助金额:
$ 20万 - 项目类别:
Continuing Grant
Collaborative Research: Scalable Multiscale Models for the Cerebrovasculature: Algorithms, Software and Petaflop Simulations
合作研究:可扩展的脑血管多尺度模型:算法、软件和千万亿次模拟
- 批准号:
0904288 - 财政年份:2009
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
Multiscale Modeling of Flow over Functionalized Surfaces: Algorithms and Applications
功能化表面流动的多尺度建模:算法和应用
- 批准号:
0852948 - 财政年份:2009
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
Multiscale Models and Petaflops Simulations on the Human Brain Vascular Network
人脑血管网络的多尺度模型和千万亿次模拟
- 批准号:
0845449 - 财政年份:2008
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
International Conference on Spectral and High-Order Methods 2009 - ICOSAHOM'09; June 2009, Trondheim, Norway
2009 年光谱和高阶方法国际会议 - ICOSAHOM09;
- 批准号:
0839866 - 财政年份:2008
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
CI-TEAM Implementation Project: Collaborative Research: Training Simulation Scientists in Advanced Cyberinfrastructure Tools and Concepts
CI-TEAM 实施项目:协作研究:培训模拟科学家掌握先进的网络基础设施工具和概念
- 批准号:
0636336 - 财政年份:2006
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
AMC-SS: A Multi-Element Generalized Polynomial Chaos Method for Modeling Uncertainty in Flow Simulations
AMC-SS:一种用于流体仿真中不确定性建模的多元素广义多项式混沌方法
- 批准号:
0510799 - 财政年份:2005
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
A Stochastic Molecular Dynamics Method for Multiscale Modeling of Blood Platlet Pheonmena
血小板现象多尺度建模的随机分子动力学方法
- 批准号:
0506312 - 财政年份:2005
- 资助金额:
$ 20万 - 项目类别:
Continuing Grant
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