Guest Editors:
Nele Moelans: KU Leuven
Talha Qasim Ansari: KU Leuven
Anil Kunwar: Silesian University of Technology
Materials Theory is calling for submissions to our Collection on Machine learning assisted morphology prediction and microstructure analysis.
Submission Status: Closed | Submission Deadline: Closed
This collection is no longer accepting submissions
The topics included in this collection are:
• Machine learning techniques applied to morphology prediction, trained based on simulations and/or experimental data
• Utilization of neural networks to solve the partial differential equations behind microstructure and multi-phase flow simulation models
• Design of machine learning based algorithms to handle efficiently high-dimensional input data for multi-component and multi-phase microstructure and fluid flow evolution models
• Employment of data-driven methods to estimate input material properties in morphology evolution models through inverse design approach
• Use of machine learning techniques to extract information from and classify microstructures and morphologies
Applications of interest include, but are not limited to: solidification, grain growth, precipitation, twinning, dislocation dynamics and plasticity, spinodal decomposition, multi-phase flow, recrystallization, corrosion, infiltration, electromigration, diffusion, microstructure reconstruction and additive manufacturing