10 papers
A plausible Parametrization of Modal Basis for Dynamical Systems Analysis
Sebastian Rodriguez, Sergio Torregrosa, Alicia Cordero +3
In the field of solid dynamics, knowing the corresponding modal basis of the system is capital, in order to improve design with respect to a desired dynamical behavior, such as avo…
Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
M. Gorpinich, B. Moya, S. Rodriguez +6
Simulating complex unsteady physical phenomena relies on detailed mathematical models, simulated for instance by using the Finite Element Method (FEM). However, these models often…
Stress-constrained Topology Optimization for Metamaterial Microstructure Design
Yanda Chen, Sebastian Rodriguez, Beatriz Moya +1
Although stress-constrained topology optimization has been extensively studied in structural design, the development of optimization frameworks to enable the creation of metamateri…
The M-Tensor Format: Optimality in High Dimensional Regression for Nonlinear Models with Scarce Data
Rémi Cloarec, Sebastian Rodriguez, Xavier Kestelyn +1
We present a nonlinear regression framework based on tensor algebra tailored to high dimensional contexts where data is scarce. We exploit algebraic properties of a partial tensor…
Rank Reduction AutoEncoders for Mechanical Design: Advancing Novel and Efficient Data-Driven Topology Optimization
Ismael Ben-Yelun, Mohammed El Fallaki Idrissi, Jad Mounayer +2
This work presents a data-driven framework for fast forward and inverse analysis in topology optimization (TO) by combining Rank Reduction Autoencoders (RRAEs) with neural latent-s…
Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation
Mohammed El Fallaki Idrissi, Jad Mounayer, Sebastian Rodriguez +2
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 th…