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20242026
most citedRank Reduction Autoencoders

2 citations · 3 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes

Sebastian Rodriguez, Mikhael Tannous, Jad Mounayer +3

Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion and the associated int…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

cs.LG2025

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…

cs.LG2024★ 2 cited

Rank Reduction Autoencoders

Jad Mounayer, Sebastian Rodriguez, Chady Ghnatios +2

The choice of an appropriate bottleneck dimension and the application of effective regularization are both essential for Autoencoders to learn meaningful representations from unlab…