Organizers:
- Yupeng Zhang, Caltech
- Burigede Liu, University of Cambridge
- Miguel A. Bessa, Brown University
Description:
The inelastic response of solids and structures can be induced by a wide range of factors, such as finite deformation, elevated temperature, high strain rate loading, and other extreme environmental conditions. These inelastic behaviors are essentially history-dependent and often computationally intensive to model due to the numerical efforts required for convergence and the potential for instability. With advancements in computational capabilities, data-driven methods, such as deep learning and statistical approaches, have been developed to address both fundamental and applied problems in inelastic solids and structures.
Topics of interest:
This mini-symposium welcomes all relevant submissions, including but not limited to:
- Model reduction, such as homogenization of constitutive relations, and further for multiscale modeling.
- Inelastic behaviors of solids, such as (crystal) plasticity, damage, brittle and ductile fracture.
- Inelastic behaviors of structures, such as architected metamaterials.
- Inverse problems, such as characterization of inelastic material properties.
- Frameworks and algorithms of data-driven methods, such as algorithms for operator learning.
- Transfer learning, such as fine tuning of models for out-of-distribution data.