1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
RRAEDy: Adaptive Latent Linearization of Nonlinear Dynamical Systems
Jad Mounayer, Sebastian Rodriguez, Jerome Tomezyk +2
Most existing latent-space models for dynamical systems require fixing the latent dimension in advance, they rely on complex loss balancing to approximate linear dynamics, and they…
Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems
Elias Al Ghazal, Jad Mounayer, Beatriz Moya +3
Modeling and predicting the dynamics of complex multiscale systems remains a significant challenge due to their inherent nonlinearities and sensitivity to initial conditions, as we…
Variational Rank Reduction Autoencoders for Generative Thermal Design
Alicia Tierz, Jad Mounayer, Beatriz Moya +1
Generative thermal design for complex geometries is fundamental in many areas of engineering, yet it faces two main challenges: the high computational cost of high-fidelity simulat…
Variational Rank Reduction Autoencoders
Jad Mounayer, Alicia Tierz, Jerome Tomezyk +2
Deterministic Rank Reduction Autoencoders (RRAEs) enforce by construction a regularization on the latent space by applying a truncated SVD. While this regularization makes Autoenco…