About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Data-Driven Learning of Constitutive Laws and Material Parameter: from Molecular Dynamics to Continuum Models |
Author(s) |
Marta D'Elia |
On-Site Speaker (Planned) |
Marta D'Elia |
Abstract Scope |
In this talk I will present a machine learning technique to obtain accurate surrogates that reproduce molecular dynamics (MD) simulations at coarser scales. Starting from MD data of graphene at different temperatures, we first apply a coarse-graining technique to project the data onto a much coarser grid and then use coarse-grained data to train a nonlocal, continuum model that accurately reproduces MD data from a validation set. Our results for a perfect crystal and in presence of thermal noise illustrate the ability to recover material parameters and show excellent generalization properties of our learning algorithm, enabling transfer learning. |
Proceedings Inclusion? |
Undecided |