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Neural Networks Predict Forces in Jammed Granular Solids


The configuration of a typical granular solid (left), and the force network or force chain in such a material (right).

Credit: Rituparno Mandal

A team of researchers from Germany's Göttingen University and Belgium's Ghent University used machine learning and computer simulations to create a tool for predicting force chains within granular solids.

The researchers showed that graph neural networks can be trained in a supervised manner to anticipate the position of force chains that manifest while deforming a granular system, provided an undeformed static structure.

Said Göttingen's Peter Sollich, "The efficiency of this new method is surprisingly high for different scenarios with varying system size, particle density, and composition of different particles types. This means it will be useful in understanding force chains for many types of granular matter and systems."

From Göttingen University (Germany)
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


 

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