Extreme Mechanics Letters (EML) Special Issue: Call for Paper
Machine Learning and Mechanics
Over the past decade, there has been a growing interest in applying machine learning techniques to problems in mechanics. From material design optimization, manufacturing, to multiscale modelling, to real time prediction and autonomy, data mining and analysis, machine learning techniques have had a massive impact in the field.
Sharing our recent article in Nature that uncovers a surprising aspect of the mechanics of epithelial tissues, termed ‘active superelasticity’, which allows them to undergo extreme reversible deformations under constant tension - https://www.nature.com/articles/s41586-018-0671-4 (read-only link: https://rdcu.be/batkj)
The word "extreme" seems to be "trending" a lot these days, see the recent discussions on the new journal Extreme Mechanics Letters.
My collaborator Ruben Sevilla at Swansea and I were interested in very curved crack paths that develop in nature and have been replicated experimentally in thin films attached to elastic substrates.
There have been several discussions on "Extreme Mechanics" in recent weeks and I would like to extend this topic to "Extreme Computing". As we develop materials that are more complex, hierarchical and are spanning multiple spatial scales, we will need computational tools that can describe them well. Fluid dynamics community has long time ago embraced large-scale computing of conservation laws of mass, momentum and energy. In mechanics of materials, large-scale computing is still in infancy.
