Machine learning based plasticity modeling
Event details
| Date | 09.12.2021 |
| Hour | 16:00 › 17:00 |
| Speaker | Prof. Dirk Mohr - ETHZ |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract: Machine learning offers a data-driven approach to the development of constitutive models as an alternative to classical physics-based modeling. Recent applications of machine learning in the context of metal plasticity are presented ranging from temperature and rate dependent hardening laws to 3D constitutive models for anisotropic solids. In addition to developing mechanics-specific neural network architectures, new robot-assisted experimental procedures are presented that generate “big data” for the identification of machine-learning based plasticity and failure models from experiments.
Practical information
- General public
- Registration required
Organizer
- EPFL-ETHZ
Contact
- EPFL : Prof. John Kolinski, [email protected] ETHZ : Prof. George Haller, [email protected]