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Symposium 3.3

Data-Driven Approaches for Complex Multiphysics Systems, Structures, and Materials


Organizers

Juner Zhu, Massachusetts Institute of Technology
Emma Lejeune, Boston University
M. Khalid Jawed, University of California, Los Angeles
Hongyi Xu, University of Connecticut

Description

Advanced engineered systems, structures, and materials are getting smart, resilient, integrated – and overall, increasingly complex. Typical examples include electrochemical energy storage systems, lattice structures, biological materials, deployable structures, and soft robots. In these examples, an important source of complexity is the coupling of multiple physical effects, such as mechanics, chemical reactions, mass and heat transfer, etc. In addition, many of these examples require accurate modeling and prediction at multiple spatial and temporal scales. A large number of variables and degrees of freedom make it a daunting task to characterize, manipulate, and design the materials, structures, and systems. In the past, physics-based or first-principle-based theories achieved great successes but gradually suffered from the “curse of dimensionality.” Recently, many data-driven approaches, particularly machine learning, have shown prominent advantages in dealing with such high-dimensional problems. In this symposium, we welcome applications of different types of data-driven approaches to solve real-world problems in order to trigger valuable discussions on this promising tool. At the same time, the greater scientific community has recognized that data-driven approaches are usually agnostic and prone to unphysical failure. Therefore, we particularly encourage investigations on this issue and attempts of combining data-driven approaches with physics-based theories.