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Stochastic Methods and Data-Driven Approaches in Computational Mechanics

Johann Guilleminot, Duke University

Michael Shields, Johns Hopkins University

Kirubel Teferra, U.S. Naval Research Laboratory

Lori Graham-Brady, Johns Hopkins University

This symposium aims at bringing together researchers involved in the development of stochastic methods and data-driven approaches for the simulation and multi-scale analysis of uncertain systems including but not limited to randomly heterogeneous materials. Contributions to the following topics are specifically encouraged:
- Applications in multiscale mechanics;
- Algebraic, functional and morphological representations in high dimensional spaces;
- Concurrent or sequential coupling of stochastic models defined at different scales;
- Data-driven modeling, including surrogate/reduced order model development;
- Incorporation of data mining/deep learning techniques;
- Simulation algorithms for stochastic processes, random fields and random sets;
- Statistical inverse identification of multiscale systems;
- Stochastic (space/time) homogenization and related numerical methods;
- Validation methodologies;
- Uncertainty quantification for Integrated Computational Materials Engineering methodologies.