CALL FOR ABSTRACTS

Engineering Mechanics Institute Conference Minisymposium on Probabilistic Learning, Stochastic Optimization, and Digital Twins

The submission website is at: https://www.emi-conference.org/program/call-abstracts and during the submission, please select this Minisymposium:
MS 035: Probabilistic Learning, Stochastic Optimization, and Digital Twins.

Organizers:
Prof. Amir H Gandomi, University of Technology Sydney, Australia
Prof. Roger Ghanem, University of Southern California, USA
Prof. Christian Soize, Université Gustave Eiffel, France

Aim and Scope:
Engineering design problems in the real world are inherently complex, high-dimensional, and uncertain. The coexistence of discrete and continuous design variables, coupled with the stochastic nature of engineering systems, renders traditional deterministic and derivative-based optimization methods insufficient. Recent advances in probabilistic learning, stochastic optimization, and digital twins have provided new paradigms for tackling such challenges, enabling robust decision-making and adaptive system modelling under uncertainty.

This Mini-Symposium aims to bring together leading researchers and practitioners to discuss recent developments and applications of these methods in engineering mechanics, structural systems, and applied sciences. The focus will be on integrating probabilistic reasoning, intelligent search algorithms, and data-driven approaches to improve the reliability, efficiency, and interpretability of computational models in design, monitoring, and control. Topics of interest include but are not limited to probabilistic deep learning, stochastic and robust optimization, evolutionary computation under uncertainty, statistical inverse problems, surrogate modelling, and digital twin technologies for both product and process engineering.

By fostering interdisciplinary dialogue across computational mechanics, optimization, and artificial intelligence, this Mini-Symposium seeks to highlight emerging tools and theories that enable resilient, data-driven, and uncertainty-aware engineering systems for the next generation of intelligent design and maintenance frameworks.