[SY-L3] Discrete dislocation dynamics simulations of complexity in crystal plasticity: strain burst statistics and machine learning
Invited
First, I will present an overview of our recent studies focusing on the statistical properties of strain avalanches in crystal plasticity as observed in discrete dislocation dynamics (DDD) simulations. These encompass various scenarios, including two and three dimensional DDD models, and considering systems with and without an additional quenched pinning field (due to, e.g., precipitates) interacting with the dislocations. I discuss the results from the perspective of two main mechanisms affecting the nature of dislocation dynamics and hence the deformation process: dislocation (de)pinning and dislocation (un)jamming.
Second, I will briefly present our very recent efforts to apply machine learning to predict the properties of the stress-strain curves of individual microscale samples using features of the initial, pre-exisiting dislocation network as input. The resulting predictability of the deformation process is found to evolve with strain in a non-monotonic fashion, something we attribute to the stochastic nature of the deformation avalanches.
Second, I will briefly present our very recent efforts to apply machine learning to predict the properties of the stress-strain curves of individual microscale samples using features of the initial, pre-exisiting dislocation network as input. The resulting predictability of the deformation process is found to evolve with strain in a non-monotonic fashion, something we attribute to the stochastic nature of the deformation avalanches.