Topics
The worshop will be focused on machine learning techniques in interaction with classical numerical simulations. All types of simulations are welcome and a large set of application fields is expected. The workshop will cover in particular the following topics (not restrictive):
- construction of parametric surrogate models from simulation outputs
- model calibration, parameters identification and inverse problems
- construction of hybrid models based on both experimental data and simulation results
- data-based pre-treatments for simulations
- deep learning for geometric design, geometry and mesh generation
- machine learning for post-treatment of simulation outputs and visualization
- Physics-informed machine learning
- Hybridization of scientific computing and machine learning
Additional details will be provided soon …