Mechanical and Civil Engineering Seminar

Mechanical and Civil Engineering Seminar Series

Title: Recent Advances in Data-Driven Computational Mechanics

Abstract:

Data-Driven (DD) computing is an emerging field of Computational Mechanics, motivated by recent technological advances in experimental measurements, the development of highly predictive computational models, advances in data storage and data processing, which enable the transition from a material data-scarce to a material data-rich era. The predictive capability of DD simulations is contingent on the quality of the material data set, i.e. its ability to closely sample all the strain-stress states in the phase space of a given mechanical problem. In this study, we develop a methodology for increasing the quality of an existing material data set in an adaptive fashion. Leveraging the formulation of the problems treated with the DD paradigm as distance minimization problems, we identify, using unsupervised learning, regions in phase space with poor data coverage, and target them with additional grain-scale physics-based simulations. The method is applied in a multiscale data-driven framework to predict the behavior of complex history-dependent materials. In contrast to continuum phenomenological models and standard multiscale techniques, the approach is parameter-free and physics-based. Meanwhile, it has the salient feature of providing "real-time" information of the errors, allowing for increasing its accuracy to a priori, user-specified levels.

Bio:

Anna Gorgogianni is a postdoctoral scholar in the Andrade group at the California Institute of Technology. She earned a Diploma in Civil Engineering from the Aristotle University of Thessaloniki (Greece) in 2014, where she graduated as a valedictorian, and a Ph.D. from the University of Minnesota in 2021. Her Ph.D. research focused on the continuum finite element modeling of quasibrittle fracture, and on the development of mechanistic ways for addressing the issue of spurious mesh sensitivity, in both deterministic and stochastic settings. She received the best doctoral dissertation award from the Department of Civil, Environmental and Geo- Engineering of the University of Minnesota. She joined the Complex Systems Modeling Group at Caltech in September 2021 as the Drinkward fellow, to expand her research to the areas of geomechanics, discrete element modeling, data-driven computational mechanics, and multiscale modeling.

NOTE: At this time, in-person Mechanical and Civil Engineering Lectures are open to all Caltech students/staff/faculty/visitors.