Elements: Curating and Disseminating Solid Mechanics Based Benchmark Datasets

要素：整理和传播基于固体力学的基准数据集

• 批准号: 2310771
• 负责人: Emma Lejeune
• 金额: $ 45.15万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310771&HistoricalAwards=false
• 关键词: Elements; Curating; Disseminating; Solid; Mechanics

From automobile safety to surgical planning, predicting the mechanical behavior of materials under loading is an essential step in engineering design. However, current approaches to predicting mechanical behavior are often very slow, where single simulations can take hours or even months to run. Motivated by this limitation, recent research has leveraged machine learning techniques to rapidly predict the behavior of engineered systems and create predictive models directly from data. Though these emerging techniques have enormous potential to benefit society, current strategies for creating machine learning models from mechanical data remain ad hoc. The goal of this research project is to change this through disseminating large mechanics-based datasets and associated scientific resources to the broader community. In addition, this project provides resources for a cultural shift within the broader mechanics community towards data and code dissemination practices that will allow the mechanics research community to make collective research progress more efficiently. The objective of this work is to create and disseminate open source benchmark datasets that focus on two broad classes of mechanical challenge: nonlinear material behavior (e.g., fracture of engineered composites), and nonlinear structural behavior (e.g., large deformation behavior and buckling of engineered metamaterials). In conjunction with dataset dissemination, this work defines and disseminates core challenge problems around emerging and unresolved areas where methodological innovation would significantly move the solid mechanics field forward such as: (1) full-field prediction for nonlinear mechanics, (2) representation of complex geometries for mechanical prediction, (3) mechanics-specific out of distribution generalization, and (4) uncertainty quantification in mechanical problems. Finally, this project includes an “Open Access Mechanics Dataset Website and Educational Resource” component to collate open access mechanics datasets from the broader research community.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the NSF Division of Civil, Mechanical and Manufacturing Innovation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从汽车安全到手术规划，预测材料在负载下的机械行为是工程设计中的重要步骤。然而，当前预测机械行为的方法通常非常缓慢，单个模拟可能需要数小时甚至数月的时间才能运行。为了克服这一限制，最近的研究利用机器学习技术来快速预测工程系统的行为，并直接从数据创建预测模型。尽管这些新兴技术具有造福社会的巨大潜力，但目前从机械数据创建机器学习模型的策略仍然是临时的。 .这个研究项目的目标是通过向更广泛的社区传播基于力学的大型数据集和相关科学资源来改变这一状况。此外，该项目还为更广泛的力学社区内的文化转变提供资源，以实现数据和代码传播实践，从而使力学研究社区能够进行集体合作。这项工作的目标是创建和传播开源基准数据集，重点关注两大类机械挑战：非线性材料行为（例如工程复合材料的断裂）和非线性结构行为（例如大型材料）。结合数据集传播，这项工作定义并传播了围绕新兴和未解决领域的核心挑战问题，这些领域的方法创新将显着推动固体力学领域的发展，例如：（1）全场预测非线性力学，（2）用于机械预测的复杂几何形状的表示，（3）特定于机械的分布概括，以及（4）机械问题中的不确定性量化最后，该项目包括“开放获取”。力学数据集网站和教育资源”组件，用于整理来自更广泛研究界的开放获取力学数据集。该奖项由 NSF 高级网络基础设施办公室颁发，并得到 NSF 土木、机械和制造创新部门的共同支持。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。