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neural 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, โ€œUncovering stress fields and defects distributions in graphene using deep neural networksโ€: 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.

New book: Nonlinear Mechanics for Composite Heterogeneous Structures

Submitted by GEStavroulakis on
education
finite elements
multiscale modeling
contact mechanics
xfem
homogenization
Data-driven mechanics
neural networks

Our new book in structural engineering and mechanics is released!

Georgios Drosopoulos, Georgios Stavroulakis,

Nonlinear Mechanics for Composite Heterogeneous Structures, CRC Press, 2022

https://www.routledge.com/Nonlinear-Mechanics-for-Composite-Heterogeneous-Structures/Drosopoulos-Stavroulakis/p/book/9780367861551

SciANN: Scientific computations and physics-informed deep learning using artificial neural networks

Submitted by haghighat on
research
deep learning
neural networks
physics-informed
elasticity
Plasticity
variational
PINN
regression

Interested in deep learning, scientific computations, solution, and inversion methods for PDE? 

Check out the preprint at: 

https://www.researchgate.net/publication/341478559_SciANN_A_Keras_wrappโ€ฆ

 

 

Some problems are shared in our GitHub repository on how to use sciann for inversion and forward solution of:

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