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Mechanics and materials are gradually becoming data-rich due to rapid advances in experimental science and high-performance multiscale computing. There has been a growing interest in the field of solid mechanics for developing data-driven and learning-based methods to characterize, understand, model, and design material/structural systems. With data-driven approaches, it is possible to remove/relax the need for ad hoc constitutive models for describing the material behavior, to achieve fast multi-scale computation for structures as well as to generate optimal designs. However, data generation/collection is still a challenging task, both experimentally and numerically. The nonlinear, path-dependent, stochastic, multi-scale, and multi-physics nature of advanced material/structural systems poses significant challenges on existing data-driven tools and theory. Furthermore, many open questions, such as how to best prescribe known physics to the data-driven framework, how to extract physics/knowledge from the big data, as well as how to achieve system-level material/structural design, remain to be answered.
The aim of this symposium is to provide a platform for scientists to exchange ideas, experiences, knowledge, and data in the rapidly growing field of data-driven mechanics. The major goal is to address the above-mentioned challenges in order to enable robust and reliable data-driven tools in materials development and structural design