Efficient Bayesian Multi-fidelity Schemes for Analysis and Design of Complex Multiphysics Systems

用于复杂多物理场系统分析和设计的高效贝叶斯多保真度方案

基本信息

项目摘要

The consideration of multiple physical fields is of paramount importance in the design process of engineering systems. Computational tools for analysis, design, and optimization of structures have evolved to account for these effects and enable the study of such complex systems. Nevertheless, a purely deterministic analysis can lead to unsatisfactory and unreliable results if any component of the models employed cannot be precisely determined or exhibits random variability. Such epistemic and aleatoric uncertainties are encountered in the overwhelming majority of real-world systems. The predictive capabilities of computational models, as well as the resilience of the systems designed, can be significantly improved if a probabilistic point of view is adopted and the uncertainties in the input parameters are accounted for in the model. Although many strategies for uncertainty quantification (UQ) have been proposed in recent years, current approaches exhibit poor scalability with the stochastic dimension and require an exuberant number of evaluations of the expensive, nonlinear forward model.The challenge we propose to undertake in this project is the development of a novel and efficient UQ framework that can be used for analysis and design of complex, nonlinear, multiphysics models with high stochastic dimension. Applications will involve strongly coupled problems such as fluid-structure-interaction. The proposed set of UQ methods will be able to handle complex, real-world systems, characterized by high-dimensional parametric uncertainties. The methods developed will provide certifiable estimates for the statistics of the output quantities of interest, as well as sensitivity measures for the uncertain model input parameters. The hitherto prohibitive computational costs associated with UQ in such complex and challenging settings will be mitigated by rigorously incorporating information from inexpensive, lower-fidelity models. These are combined with a few, intelligently selected evaluations of the expensive, high-fidelity model, in order to obtain accurate estimates at a fraction of the cost compared to current UQ methods. Moreover, by adopting a Bayesian approach, credible intervals can be computed which quantify the confidence in the estimates as well as guide adaptive refinements. In addition, the UQ approach will also serve as the basis for the development of a novel stochastic optimization framework for the design of complex systems in the presence of uncertainties. The proposed methods are very general and will be applicable to a wide range of problems. As example, large-scale, nonlinear problems arising in cardiovascular biomechanics will be used to demonstrate capabilities and efficiency of the methods.The proposal directly addresses several of the most fundamental questions posed by the priority program. A set of benchmark problems will be developed that will enable comparison as well as cross-pollination of different perspectives.
在工程系统的设计过程中,考虑多个物理场是至关重要的。用于分析、设计和优化结构的计算工具已经发展到能够解释这些影响并使研究这种复杂系统成为可能。然而,如果所用模型的任何组成部分不能被精确确定或表现出随机变异性,纯粹的确定性分析可能会导致不满意和不可靠的结果。这种认识性和任意性的不确定性在绝大多数真实世界的系统中都会遇到。如果采用概率观点并在模型中考虑输入参数中的不确定性,则可以显著提高计算模型的预测能力以及所设计系统的弹性。尽管近年来已经提出了许多不确定量化策略,但现有方法对随机维度的可扩展性较差,并且需要对昂贵的非线性正向模型进行大量的评估。我们在该项目中提出的挑战是开发一个新的、高效的UQ框架,该框架可以用于分析和设计具有高随机维度的复杂、非线性、多物理模型。应用将涉及到强耦合问题,如流体-结构-相互作用。建议的一套UQ方法将能够处理具有高维参数不确定性的复杂的真实世界系统。所开发的方法将为感兴趣的输出量的统计数据提供可证明的估计,并为不确定的模型输入参数提供灵敏度测量。在如此复杂和具有挑战性的环境中,与UQ相关的迄今令人望而却步的计算成本将通过严格纳入来自廉价、低保真模型的信息而得到缓解。这些与对昂贵的高保真模型的几个智能选择的评估相结合,以便以当前UQ方法的一小部分成本获得准确的估计。此外,通过采用贝叶斯方法,可以计算可靠的区间,量化估计中的置信度,并指导自适应改进。此外,UQ方法还将作为开发一种新的随机优化框架的基础,用于在存在不确定性的情况下设计复杂系统。所提出的方法是非常普遍的,将适用于广泛的问题。例如,心血管生物力学中出现的大规模、非线性问题将被用来展示方法的能力和效率。该提议直接解决了优先计划提出的几个最基本的问题。将制定一套基准问题,以便对不同观点进行比较和交叉授粉。

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 }}

Professor Phaedon-Stelios Koutsourelakis, Ph.D.其他文献

Professor Phaedon-Stelios Koutsourelakis, Ph.D.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('Professor Phaedon-Stelios Koutsourelakis, Ph.D.', 18)}}的其他基金

Enabling efficient and certifiable solutions in diagnostic biomechanics by rephrasing model-based inverse problems.
通过重新表述基于模型的逆问题,在诊断生物力学中实现高效且可认证的解决方案。
  • 批准号:
    499746055
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

相似国自然基金

多元纵向数据与复发事件和终止事件的Bayesian联合模型研究
  • 批准号:
    82173628
  • 批准年份:
    2021
  • 资助金额:
    52 万元
  • 项目类别:
    面上项目
三维地质模型约束下地球化学场的Bayesian-MCMC推断
  • 批准号:
    42072326
  • 批准年份:
    2020
  • 资助金额:
    63 万元
  • 项目类别:
    面上项目
基于Bayesian Kriging模型的压射机构稳健优化设计基础研究
  • 批准号:
    51875209
  • 批准年份:
    2018
  • 资助金额:
    59.0 万元
  • 项目类别:
    面上项目
X射线图像分析中的MCMC-Bayesian理论与计算方法研究
  • 批准号:
    U1830105
  • 批准年份:
    2018
  • 资助金额:
    62.0 万元
  • 项目类别:
    联合基金项目
基于Bayesian位移场的SAR图像精确配准方法研究
  • 批准号:
    41601345
  • 批准年份:
    2016
  • 资助金额:
    19.0 万元
  • 项目类别:
    青年科学基金项目
多结局Bayesian联合生存模型及糖尿病并发症预测研究
  • 批准号:
    81673274
  • 批准年份:
    2016
  • 资助金额:
    50.0 万元
  • 项目类别:
    面上项目
基于Meta流行病学和Bayesian方法构建针刺干预无偏倚风险效果评价体系研究
  • 批准号:
    81403276
  • 批准年份:
    2014
  • 资助金额:
    23.0 万元
  • 项目类别:
    青年科学基金项目
BtoC电子商务中基于分层Bayesian网络的信任与声誉计算理论研究
  • 批准号:
    71302080
  • 批准年份:
    2013
  • 资助金额:
    20.0 万元
  • 项目类别:
    青年科学基金项目
基于Bayesian网络的坚硬顶板条件下煤与瓦斯突出预警控制机理研究
  • 批准号:
    51274089
  • 批准年份:
    2012
  • 资助金额:
    80.0 万元
  • 项目类别:
    面上项目
Bayesian实物期权及在信用风险决策中的应用
  • 批准号:
    71071027
  • 批准年份:
    2010
  • 资助金额:
    23.0 万元
  • 项目类别:
    面上项目

相似海外基金

Rapid, Scalable, and Joint Assessment of Seismic Multi-Hazards and Impacts: From Satellite Images to Causality-Informed Deep Bayesian Networks
地震多重灾害和影响的快速、可扩展和联合评估:从卫星图像到因果关系深度贝叶斯网络
  • 批准号:
    2242590
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Development and application of learning theory for uncertainty in Bayesian deep learning based on multi-objective optimization
基于多目标优化的贝叶斯深度学习不确定性学习理论发展及应用
  • 批准号:
    23K16948
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Interpretable Bayesian Non-linear statistical learning models for multi-omics data integration
用于多组学数据集成的可解释贝叶斯非线性统计学习模型
  • 批准号:
    10714882
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Multiscale, Multi-fidelity and Multiphysics Bayesian Neural Network (BNN) Machine Learning (ML) Surrogate Models for Modelling Design Based Accidents
用于基于事故建模设计的多尺度、多保真度和多物理场贝叶斯神经网络 (BNN) 机器学习 (ML) 替代模型
  • 批准号:
    2764855
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Studentship
Scalable Bayesian Statistical Machine Learning for Multi-modal Data with Applications to Multiple Sclerosis
多模态数据的可扩展贝叶斯统计机器学习及其在多发性硬化症中的应用
  • 批准号:
    2740724
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Studentship
A Bayesian Approach to Distributed Estimation for Multi-Object Systems
多目标系统分布式估计的贝叶斯方法
  • 批准号:
    FT210100506
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    ARC Future Fellowships
Construction of material exploration system using automatic first-principles calculation and multi-objective Bayesian optimiziation
利用自动第一性原理计算和多目标贝叶斯优化构建材料探索系统
  • 批准号:
    21K14401
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
CAREER: Forward and Inverse Uncertainty Quantification of Cardiovascular Fluid-Structure Dynamics via Multi-fidelity Physics-Informed Bayesian Geometric Deep Learning
职业:通过多保真物理信息贝叶斯几何深度学习对心血管流体结构动力学进行正向和逆向不确定性量化
  • 批准号:
    2047127
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Bayesian fusion models based on multi-level constraints and multiple criteria in image processing and computer vision
图像处理和计算机视觉中基于多级约束和多准则的贝叶斯融合模型
  • 批准号:
    RGPIN-2016-04578
  • 财政年份:
    2020
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Novel Bayesian statistical tools for integrating multi-omics data to help elucidate the genomic etiology of complex phenotypes
用于整合多组学数据的新型贝叶斯统计工具,有助于阐明复杂表型的基因组病因学
  • 批准号:
    10671498
  • 财政年份:
    2020
  • 资助金额:
    --
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了