Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911316098154496 |
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| author | Idrissi, Mohammed El Fallaki Mounayer, Jad Rodriguez, Sebastian Meraghni, Fodil Chinesta, Francisco |
| author_facet | Idrissi, Mohammed El Fallaki Mounayer, Jad Rodriguez, Sebastian Meraghni, Fodil Chinesta, Francisco |
| contents | This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding parametric solutions given as a reduced basis. To achieve this, two Rank Reduction Autoencoders (RRAEs) are employed, one for encoding and generating the design or geometry, and the other for encoding the sparse Proper Generalized Decomposition (sPGD) mode solutions. These models are linked in the latent space using regression techniques, allowing efficient transitions between design and their associated sPGD modes. By empowering design exploration and optimization, this framework also advances digital and hybrid twin development, enhancing predictive modeling and real-time decision-making in engineering applications. The developed framework is demonstrated on two-phase microstructures, in which the multiparametric solutions account for variations in two key material parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation Idrissi, Mohammed El Fallaki Mounayer, Jad Rodriguez, Sebastian Meraghni, Fodil Chinesta, Francisco Computational Engineering, Finance, and Science Artificial Intelligence This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding parametric solutions given as a reduced basis. To achieve this, two Rank Reduction Autoencoders (RRAEs) are employed, one for encoding and generating the design or geometry, and the other for encoding the sparse Proper Generalized Decomposition (sPGD) mode solutions. These models are linked in the latent space using regression techniques, allowing efficient transitions between design and their associated sPGD modes. By empowering design exploration and optimization, this framework also advances digital and hybrid twin development, enhancing predictive modeling and real-time decision-making in engineering applications. The developed framework is demonstrated on two-phase microstructures, in which the multiparametric solutions account for variations in two key material parameters. |
| title | Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11748 |