6 papers
Hybrid Machine Learning and Physical Modeling of Feedstock Deformation During Robotic 3D Printing of Continuous Fiber Thermoplastic Composites
Chady Ghnatios, Kazem Fayazbakhsh
Feedstock deformation during 3D printing of continuous fiber composites is a critical challenge in path planning and a main driver in the generation of manufacturing defects. The p…
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
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…
Machine Learning (ML) based Reduced Order Modeling (ROM) for linear and non-linear solid and structural mechanics
Mikhael Tannous, Chady Ghnatios, Eivind Fonn +2
Multiple model reduction techniques have been proposed to tackle linear and non linear problems. Intrusive model order reduction techniques exhibit high accuracy levels, however, t…
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…