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Showing posts with label Forces. Show all posts
Showing posts with label Forces. Show all posts

Wednesday, December 07, 2022

Connecting the Joint Force as One

Northrup Grumman:   Connecting the Joint Force as One

Future mission success will lay in highly-effective Joint All-Domain Command and Control (JADC2)

‘Joining Forces’ has always been a critical factor in creating extra strength

In the future, more than ever before, mission advantage will go to military forces who can coordinate and act together across all domains. Observing, orienting, deciding and acting together, faster and with greater effect than their adversaries is the next-gen technological edge for Joint Forces. And, whether Joint Forces are across United States military branches including the U.S. Air Force, U.S. Army, U.S. Navy, U.S. Marines, U.S. Space Force, or in partnership with allies―future mission success will lay in highly-effective Joint All-Domain Command and Control (JADC2).

Ars Technicast Special Edition: The Internet of Military Things

For decades, the US Department of Defense has been trying to make the tools we use to fight battles more like the tools we use in peacetime with more connectivity, more data, and more smarts. The difference this time around, is that technology has caught up to the point where what we can do in real life almost mirrors what we can do in movies. To learn more, listen to Scott Stapp, Chief Technology Officer, Northrop Grumman on Ars Technica’s podcast.

(Podcast) 

Wednesday, September 28, 2022

Neural Networks Predict Forces in Jammed Granular Solids

 Unexpected application for a neural network.

Neural Networks Predict Forces in Jammed Granular Solids  By Göttingen University (Germany), September 8, 2022

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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