Computational and machine learning methods for model reduction, uncertainty propagation, and parameter identification in fluid and solid mechanics

流体和固体力学中模型简化、不确定性传播和参数识别的计算和机器学习方法

• 批准号: RGPIN-2021-02693
• 负责人: Soulaïmani, Azzeddine
• 金额: $ 3.35万
• 依托单位: École de technologie supérieure
• 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: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754512
• 关键词: Computational; machine; learning; methods; model

Most fluid and solid problems in engineering modeling are described by time-dependent and parametrized nonlinear partial differential equations. Their resolution with traditional computational mechanics methods may be too expensive, especially in the context of predictions with uncertainty quantification or optimization, to allow for rapid predictions. We propose to investigate advanced machine learning methods aimed at representing high-fidelity computational models by means of reduced-dimension surrogate ones. In addition, different approaches will be studied for the uncertainty quantification. Indeed, reliable modeling predictions of natural and industrial processes should include quantification of the uncertainties that may arise from various sources. This program also supports a continuous interest in parallel computing to enable rapid predictions for large-scale simulations. While these proposed activities are geared towards developing new approaches and algorithms, the research program is also application-driven. We will focus on two challenging engineering applications, motivated by real needs. However, the proposed methods  will not be restricted to these applications. - Probabilistic flooding maps using machine learning: Floods are among the costliest natural disasters. Governments and agencies are therefore required to develop reliable and accurate maps of flood risk areas as part of their preventive measures. The main objective here is to develop predictive tools that combine advanced computational methods with machine learning to establish accurate maps with probabilistic information; and, in the case of an extreme emergency event, to allow rapid predictions, almost in real-time. - Physical parameter identification: The constitutive parameters used in the modeling of complex systems are often associated with high degrees of uncertainties. Inverse analysis provides a way to identify these parameters. We consider the identification and optimization of the numerous parameters involved in the selective laser melting (SLM) additive manufacturing process. Additive manufacturing is a modern technology that has been used across a diverse range of industries, including automotive, aerospace and medical, among others. It is anticipated that this research program will contribute to the advancement of knowledge about several aspects of numerical modeling, machine learning and parameter optimization, and add to the analysis of uncertainties in hydraulics and additive manufacturing. The training of highly qualified personnel (5 Ph.D.s, one postdoctoral fellow, 2 M.SC students and 5 undergraduate students) will be beneficial to society as a whole, as well as to relevent industries in Canada. There is a great potential for a number of innovative publications and collaborations with industrial partners. This research will also result in the development of high-performance codes that can be used for even more research and applied projects.

工程建模中的大多数流体和固体问题都是通过与时间相关的参数化非线性偏微分方程来描述的，使用传统计算力学方法解决这些问题可能成本太高，尤其是在不确定性量化或优化的预测背景下，无法进行快速预测。我们提出了旨在通过降维替代模型来表示高保真计算模型的先进机器学习方法，此外，还将研究不同的方法来进行不确定性量化。包括对各种来源可能产生的不确定性进行量化，该计划还支持对并行计算的持续关注，以实现大规模模拟的快速预测。虽然这些拟议的活动旨在开发新的方法和算法，但该研究计划也我们将重点关注两个具有挑战性的工程应用，但所提出的方法将不仅限于这些应用 - 使用机器学习的概率洪水地图：洪水是损失最惨重的自然灾害之一。因此，需要开发可靠且准确的洪水风险区域地图，作为其预防措施的一部分，这里的主要目标是开发将先进计算方法与机器学习相结合的预测工具，以建立具有概率信息的准确地图； - 物理参数识别：复杂系统建模中使用的本构参数通常与高度的不确定性相关，逆分析提供了一种识别这些参数的方法。 .我们考虑识别和优化选择性激光熔化 (SLM) 增材制造工艺涉及的众多参数是一项现代技术，已应用于汽车、航空航天和医疗等多个行业。将有助于提高有关数值建模、机器学习和参数优化等多个方面的知识，并增加对液压和增材制造不确定性的分析（5名博士、1名博士后、2名）。硕士生和5名本科生）将有益于整个社会以及加拿大的相关行业。许多创新出版物以及与行业合作伙伴的合作也将带来巨大的潜力。可用于更多研究和应用项目的代码。