EAGER-DynamicData: Collaborative Research: Data-driven morphing of parsimonious models for the description of transient dynamics in complex systems

EAGER-DynamicData:协作研究:数据驱动的简约模型变形,用于描述复杂系统中的瞬态动力学

基本信息

  • 批准号:
    1462254
  • 负责人:
  • 金额:
    $ 7.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-15 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

Predicting and quantifying the behavior of complex systems in engineering and science is a topic of critical importance for many areas such as design, optimization, and safety. Even more critical is the forecast of extreme responses of these systems. Rare responses that can lead to catastrophic events manifest themselves in a wide range of systems such as geophysical phenomena, power and communication networks, and epileptic incidents in brain activity, just to mention a few. In all of these cases, accurate predictions are hampered by the fact that the exact dynamics of the system in nature is often poorly understood. This poor understanding is due to the large number of essentially coupled mechanisms that operate at different temporal and spatial scales. Although it is not always essential to predict the system with high accuracy over all these different levels, it is important to understand and model the effect of the unresolved mechanisms to the degrees of freedom we want to predict. This requires a reliable knowledge of the descriptive laws for these mechanisms as well as their coupling to the degrees-of-freedom of interest and this is clearly not always (well) done. This incomplete modeling of the dynamics leads to inevitable model error that is essential to be taken into account for reliable predictions. An obvious way that this can be done is by the utilization of available and dynamically incoming data. The goal of this work is the development of methods and algorithms to extend the capability for data-driven morphing (that is, data-driven adaptation) of parsimonious models. These will be able to adequately capture the instantaneously most significant dynamics of the system and utilize them to inexpensively perform informative prediction and uncertainty quantification. Such a development will be of critical importance for many fields where the modeling and predictive capacity is limited by the inadequate understanding of the underlying physical mechanisms.The aim of this proposal is to link machine learning with model reduction in a data-stream-driven environment, in order to formulate fundamentally novel methods for the probabilistic forecast of complex stochastic systems. Of particular interest is the quantification and prediction of extreme responses, by relying exclusively on the utilization of available data and with the minimum use of equations (or high fidelity solvers), if these are available. The effort is driven by the presence of serious obstacles associated with the prediction of such features in complex dynamical systems: non-negligible model error in the descriptive laws (if these are available), prohibitive cost for real time computations, sparse data or data with non-negligible error, and transient dynamics. These difficulties are manifest at a time when there is a great need for understanding and prediction of extreme responses in contexts such as climate dynamics, nonlinear waves, and networks of high dimensionality. The aim is to address several of theses challenges by constructing new stochastic prediction methods that will extend the existing state-of-the-art for data-driven modeling and prediction through the implementation (and appropriate extension) of machine learning / data mining techniques and the combination with stochastic order reduction and uncertainty quantification methods that dynamically adapt the reduced order subspace according to the dynamics. These efforts will be guided by a proof-of-concept application involving prediction of extreme, localized events in nonlinear waves. Adaptive reduced order models driven by data will be a key element for the inference of critical dynamical properties, which are otherwise "buried" in the complex responses. By linking machine learning techniques to adaptive reduced order models our research will catalyze new domains of numerical/mathematical analysis and it will extend the reach of more conventional mathematics-assisted modeling beyond some of its current limits.
预测和量化工程和科学中复杂系统的行为对于设计,优化和安全等许多领域都是至关重要的主题。更重要的是预测这些系统的极端反应。可能导致灾难性事件的罕见反应表现在广泛的系统中,例如地球物理现象,电力和通信网络,以及大脑活动中的癫痫事件,仅举几例。在所有这些情况下,准确的预测都受到事实的阻碍,即自然界中系统的确切动力学往往知之甚少。这种认识不足的原因是,在不同的时间和空间尺度上运作的大量基本上是相互关联的机制。虽然在所有这些不同的水平上以高精度预测系统并不总是必要的,但重要的是要理解和建模未解决的机制对我们想要预测的自由度的影响。这需要对这些机制的描述性定律以及它们与感兴趣的自由度的耦合有可靠的了解,而这显然并不总是(很好)做到的。这种不完整的动态建模导致不可避免的模型误差,这对于可靠的预测是必不可少的。一种显而易见的方法是利用可用的动态传入数据。这项工作的目标是开发方法和算法,以扩展简约模型的数据驱动变形(即数据驱动自适应)的能力。这些将能够充分捕捉系统的瞬时最重要的动态,并利用它们来廉价地执行信息预测和不确定性量化。这样的发展将是至关重要的许多领域的建模和预测能力是有限的基础物理mechanism.The目的的理解不足,这项建议是连接机器学习与模型简化在数据流驱动的环境中,为了制定从根本上新颖的方法,复杂的随机系统的概率预测。特别令人感兴趣的是极端响应的量化和预测,完全依赖于利用现有数据,并最少使用方程(或高保真解算器),如果这些是可用的。这一努力是由与复杂动态系统中的这些特征的预测相关的严重障碍的存在所驱动的:描述性定律中不可忽略的模型误差(如果这些是可用的)、真实的时间计算的过高成本、稀疏数据或具有不可忽略的误差的数据以及瞬态动态。这些困难是显而易见的时候,有一个非常需要的理解和预测的极端响应的背景下,如气候动力学,非线性波,和网络的高维。我们的目标是通过构建新的随机预测方法来解决这些挑战,这些方法将通过机器学习/数据挖掘技术的实施(和适当的扩展)以及与随机降阶和不确定性量化方法的组合来扩展现有的数据驱动建模和预测的最新技术,这些方法根据动态动态动态地调整降阶子空间。这些工作将由一个概念验证应用程序指导,涉及非线性波中极端局部事件的预测。由数据驱动的自适应降阶模型将是推断临界动力学特性的关键因素,否则这些特性将“隐藏”在复杂的响应中。通过将机器学习技术与自适应降阶模型联系起来,我们的研究将促进数值/数学分析的新领域,并将扩展更传统的几何学辅助建模的范围,超越其当前的一些限制。

项目成果

期刊论文数量(0)
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Themistoklis Sapsis其他文献

Reconstructing flexible body vortex-induced vibrations using machine-vision and predicting the motions using semi-empirical models informed with transfer learned hydrodynamic coefficients
  • DOI:
    10.1016/j.jfluidstructs.2024.104154
  • 发表时间:
    2024-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Andreas P. Mentzelopoulos;Emile Prele;Dixia Fan;Jose del Aguila Ferrandis;Themistoklis Sapsis;Michael S. Triantafyllou
  • 通讯作者:
    Michael S. Triantafyllou

Themistoklis Sapsis的其他文献

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{{ truncateString('Themistoklis Sapsis', 18)}}的其他基金

Planning Grant: Engineering Research Center for Technologies and Design for Sustainable Offshore Aquaculture (SOA)
规划资助:可持续近海水产养殖技术与设计工程研究中心(SOA)
  • 批准号:
    1936981
  • 财政年份:
    2019
  • 资助金额:
    $ 7.5万
  • 项目类别:
    Standard Grant

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