TY  - Type of reference 
TI  - Topology optimization using artificial intelligence 
AU  - Ahmed Ait Ouchaoui
 
AU  - Mohammed Nassraoui
 
AU  - Bouchaib Radi 
AB  - An analysis of topology optimization employing deep learning, namely Generative Adversarial Networks (GANs), and topology optimization utilizing the Solid Isotropic Material with Penalization (SIMP) method is presented in this research. We describe the theoretical foundations of GANs and the SIMP technique. A cantilever beam with predetermined boundary conditions was the topic of a static study to show the practical efficacy of these methods. The structural performance parameters, such as maximal directional displacement, maximal Von Mises stress, and deformation energy. The findings show that deep learning-based topology optimization, as demonstrated by TopologyGAN, provides considerable benefits in terms of improved design correctness and computing performance. 
DO  - 10.21494/ISTE.OP.2024.1231 
JF  - Uncertainties and Reliability of Multiphysical Systems 
KW  - additive manufacturing, topology optimization, artificial intelligence, machine learning, Fabrication additive, optimisation topologique, intelligence artificielle, apprentissage machine,  
L1  - https://www.openscience.fr/IMG/pdf/iste_incertfia24v8n2_7.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2024/10/24 
SN  - 2514-569X 
TT  - L’optimisation topologique en utilisant l’intelligence artificielle 
UR  - https://www.openscience.fr/Topology-optimization-using-artificial-intelligence 
IS  - Issue 2 
VL  - 8 
ER  -