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generative adversarial networks

Uncovering stress fields and defects distributions in graphene using deep neural networks

Submitted by Nuwan Dewapriya on
research
neural networks
cracks
graphene
generative adversarial networks
molecular dynamics

 

In our latest article, “Uncovering stress fields and defects distributions in graphene using deep neural networks”: https://doi.org/10.1007/s10704-023-00704-z , we showed that conditional generative adversarial networks (cGANs) could transform complex deformation fields into stress fields by eliminating the need to evaluate elasticity distributions and develop complex nonlinear constitutive relations.

Call for Papers: Special Issue on Advances in Integrated Digital Engineering Applications

Submitted by Lee Margetts on
research
virtual reality
machine learning
Internet of Things
Digital Twin
Digital Thread
cyber-physical systems
design
Digital Manufacturing and Design
optimisation
generative adversarial networks

Dear Colleagues,

I'm current guest editor on a Special Issue of the MDPI Journal of Applied Sciences. This is a journal with an impact factor of 2.474.

The aim of this Special Issue is to explore the re-engineering of engineering through the integration of advanced digital technologies. Research papers or case studies involving any discipline of engineering are welcome. Topics may include, but are not limited to, the following:

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