Topology optimization using artificial intelligence
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 th...
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| Format: | Article |
| Language: | English |
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ISTE Group
2024-01-01
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| Series: | Incertitudes et Fiabilité des Systèmes Multiphysiques |
| Online Access: | https://www.openscience.fr/Topology-optimization-using-artificial-intelligence |
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| _version_ | 1849326631066271744 |
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| author | Ahmed Ait Ouchaoui Mohammed Nassraoui Bouchaib Radi |
| author_facet | Ahmed Ait Ouchaoui Mohammed Nassraoui Bouchaib Radi |
| author_sort | Ahmed Ait Ouchaoui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c38fff676a2b4d7abdd06b57c7e78b15 |
| institution | Kabale University |
| issn | 2514-569X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | ISTE Group |
| record_format | Article |
| series | Incertitudes et Fiabilité des Systèmes Multiphysiques |
| spelling | doaj-art-c38fff676a2b4d7abdd06b57c7e78b152025-08-20T03:48:06ZengISTE GroupIncertitudes et Fiabilité des Systèmes Multiphysiques2514-569X2024-01-0182384210.21494/ISTE.OP.2024.1231Topology optimization using artificial intelligenceAhmed Ait OuchaouiMohammed NassraouiBouchaib Radi 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.https://www.openscience.fr/Topology-optimization-using-artificial-intelligence |
| spellingShingle | Ahmed Ait Ouchaoui Mohammed Nassraoui Bouchaib Radi Topology optimization using artificial intelligence Incertitudes et Fiabilité des Systèmes Multiphysiques |
| title | Topology optimization using artificial intelligence |
| title_full | Topology optimization using artificial intelligence |
| title_fullStr | Topology optimization using artificial intelligence |
| title_full_unstemmed | Topology optimization using artificial intelligence |
| title_short | Topology optimization using artificial intelligence |
| title_sort | topology optimization using artificial intelligence |
| url | https://www.openscience.fr/Topology-optimization-using-artificial-intelligence |
| work_keys_str_mv | AT ahmedaitouchaoui topologyoptimizationusingartificialintelligence AT mohammednassraoui topologyoptimizationusingartificialintelligence AT bouchaibradi topologyoptimizationusingartificialintelligence |