CEE Seminar - Data driven modeling of mechanical systems
Over the past decade, there has been a growing interest in leveraging machine learning techniques to model complex mechanical systems. Compellingly, these techniques have become invaluable tools for applications ranging from topology optimization, to uncertainty quantification, to real-time prediction, to multi-scale modeling and beyond. Typically, researchers take either a "problem-centric" or "model-centric" approach to this work. Namely, they focus on either an overarching engineering challenge, or they focus on developing machine learning methods and model architectures. In this talk, we will present a "data-centric" approach to data driven modeling of mechanical systems. Specifically, we will discuss work where we focus on defining and curating datasets as our top priority [1]. First, we will share our work in developing and disseminating benchmark datasets for engineering mechanics problems [2, 3]. In addition to describing these datasets, we will also discuss our recent work in investigating deep learning model calibration [4]. Then, we will share our work in defining an open science based methodological foundation for data driven modeling of (bio)mechanical systems. In brief, we envision a methodological framework with three essential components: (1) open access datasets, (2) open source software to extract interpretable quantities of interest from these data, and (3) combined mechanistic and statistical models of (bio)mechanical behavior informed by these data. As an illustrative example, we will discuss our recent collaborative work in cardiac tissue engineering [5, 6]. Overall, the goal of this talk is to spark discussion and inspire future work on "data-centric" approaches to mechanical modeling.
[1] https://imechanica.org/node/25935
[2] https://open.bu.edu/handle/2144/39371
[3] https://elejeune11.github.io/
[4] https://doi.org/10.1016/j.mechmat.2023.104749
[5] https://joss.theoj.org/papers/10.21105/joss.05322
[6] https://arxiv.org/abs/2308.04610
