An artificial brain for interpreting and accelerating physics-based simulations of granular materials
How do we keep dikes safe with rising sea levels? Why are ripples formed in sand? How can we prepare for landing on Mars? At the core of these questions is the understanding of how grains, as a self-organizing material, collide flow, or get jammed and compressed. State-of-the-art computer algorithms simulate how millions of individual grains behave in different settings. However, these simulations can take a very long time, and the big data on particle motion is difficult to interpret and generalize, compared to a simulation of avalanches and free-standing sandcastles.
What are we trying to achieve and how?
In this project, we will use machine learning, our artificial brain, to (1) extract hidden links between grain, microstructural and macroscopic properties, and (2) instantly generate microstructures that satisfy macroscopic constraints, from an existing database. This workflow will be deployed on the cloud and used to find optimal microstructure/grain properties that define a ‘smart’ (responsive) granular material.
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