Organizers: Burigede Liu, Yupeng Zhang, Hossein (Amir) Salahshoor, Swarnava Ghosh
We welcome submissions to our mini-symposium. Website for 2026 SES.
The inelastic response of solids and structures can be induced by a wide range of factors, such as finite deformation, elevated temperature, aging, 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 solid and structures.
This mini-symposium welcomes all relevant submissions, including but not limited to:
1. Model reduction, such as homogenization of constitutive relations, and further for multiscale modeling.
2. Inelastic behaviors of solids, such as (crystal) plasticity, damage, brittle and ductile fracture.
3. Inelastic behaviors of structures, such as architected metamaterials.
4. Frameworks and algorithms of data-driven methods, such as algorithms for operator learning.
5. Transfer learning, such as fine tuning of models for out-of-distribution data.