Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation
This advances digital and hybrid twin development for predictive modeling and real-time decision-making in engineering, though it appears incremental as it builds on existing autoencoder and regression techniques.
The paper tackles the problem of real-time geometry generation and multiparametric approximation in simulation-based engineering by introducing the Generative Parametric Design (GPD) framework, which uses Rank Reduction Autoencoders and regression to link designs with sparse Proper Generalized Decomposition modes, demonstrated on two-phase microstructures with variations in two material parameters.
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.