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实体问题都由时间依赖性和参数化的非线性偏微分方程描述。他们用传统的计算力学方法的分辨率可能太昂贵了，尤其是在不确定性量化或优化的预测的背景下，以允许快速预测。我们建议研究旨在通过降低维替代替代替代替代替代替代模型来代表高保真计算模型的高级机器学习方法。此外，将研究不同的方法以进行不确定性定量。实际上，自然和工业过程的可靠建模预测应包括量化可能来自各种来源的不确定性。该程序还支持对并行计算的持续兴趣，以实现大规模模拟的快速预测。尽管这些拟议的活动旨在开发新的方法和算法，但研究计划也是以应用程序驱动的。我们将专注于由实际需求激发的两个挑战工程应用程序。但是，提出的方法将不限于这些应用。 - 使用机器学习的概率洪水图：洪水是最昂贵的自然灾害之一。因此，政府和机构必须为其预防测量的一部分开发可靠，准确的洪水风险区域地图。这里的主要目的是开发将高级计算方法与机器学习相结合的预测工具，以通过概率信息建立准确的地图；而且，在极端紧急事件的情况下，几乎实时允许快速预测。 - 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不确定性有关。逆分析提供了一种识别这些参数的方法。我们考虑选择性激光熔化（SLM）添加剂制造过程中众多参数的识别和优化。增材制造是一种现代技术，已在包括汽车，航空航天和医疗等领域的潜水行业中使用。可以预料，该研究计划将有助于进步有关数值建模，机器学习和参数优化的几个方面的知识，并增加了液压和添加剂制造中不确定性的分析。对高素质的人员（5博士学位，一名博士后研究员，2名M.SC学生和5名本科生）的培训将对整个社会有利，并认可加拿大的行业。许多创新出版物和与工业合作伙伴的合作有很大的潜力。这项研究还将导致开发高性能代码，这些代码可用于更多的研究和应用项目。