Elements: Curating and Disseminating Solid Mechanics Based Benchmark Datasets

Trustees Of Boston University, Boston

Investigators

Abstract

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.