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MEAM Seminar: “Open Access Benchmark Datasets and Metamodels for Problems in Mechanics”

September 20, 2022 at 10:00 AM - 11:30 AM

Metamodels, or models of models, map defined model inputs to defined model outputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to real-time prediction, to multi-scale simulation. In particular, for heterogeneous materials, metamodels are useful for exploring the influence of the (potentially massive) heterogeneous material property parameter space. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data — specifically simulations of heterogenous materials — has not been thoroughly explored. In this work, we draw inspiration from the benchmark datasets available to the computer vision research community. These benchmark datasets have both made it feasible to compare different methods for solving the same problem, and inspired new directions for method development. In response, we introduce benchmark datasets for engineering mechanics problems (for example, the Mechanical MNIST Collection https://open.bu.edu/handle/2144/39371 [1,2,3]). Then, we show some example problems that we are exploring with these datasets such as our methodology for constructing metamodels for predicting full field quantities of interest (e.g., full field displacements, stress, strain, or damage variable), and for leveraging information from multiple simulation fidelities, and for predicting out of distribution behavior. Looking forward, we anticipate that disseminating both these benchmark datasets and our computational methods will enable the broader community of researchers to develop improved techniques for understanding the behavior of spatially heterogeneous materials. We also hope to inspire others to use our datasets for educational and research purposes, and to disseminate datasets and metamodels specific to their own areas of interest (https://elejeune11.github.io/).

[1] Lejeune, E. (2020). Mechanical MNIST: A benchmark dataset for mechanical metamodels. Extreme Mechanics Letters, 36, 100659.
[2] Lejeune, E., & Zhao, B. (2020). Exploring the potential of transfer learning for metamodels of heterogeneous material deformation. Journal of the Mechanical Behavior of Biomedical Materials, 104276.
[3] Mohammadzadeh, S., & Lejeune, E. (2022). Predicting mechanically driven full-field quantities of interest with deep learning-based metamodels. Extreme Mechanics Letters, 50, 101566.

Emma Lejeune

Assistant Professor, Department of Mechanical Engineering, Boston University

Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018, and was a Peter O’Donnell, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU, Emma has received the David R. Dalton Career Development Professorship, a Computational Science and Engineering Junior Faculty Fellowship, the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division, and the American Heart Association Career Development Award. Current areas of research involve integrating data-driven and physics based computational models, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems.


September 20, 2022
10:00 AM - 11:30 AM
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Mechanical Engineering and Applied Mechanics
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street
Philadelphia, PA 19104 United States
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