Azzeddine Tiba

Azzeddine Tiba

Scientific Machine Learning Research Engineer

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

portfolio

FeCLAP

A finite element package for the simulation of laminated composite plates in elasticity and plasticity.

GitHub Repository

publications

Dynamical Data-Driven Model Order Reduction for nonlinear Fluid-Structure Interaction problems

Published in 25è Congrès Français de Mécanique, 2022

Conference paper on dynamical data-driven model order reduction for nonlinear FSI problems.

Recommended citation: Azzeddine Tiba, Thibault Dairay, Florian De Vuyst, Iraj Mortazavi, Juan Pedro Berro Ramirez. (2022). "Dynamical Data-Driven Model Order Reduction for nonlinear Fluid-Structure Interaction problems." 25è Congrès Français de Mécanique. pp. 151-158.

Non-intrusive reduced order models for partitioned fluid-structure interactions

Published in Journal of Fluids and Structures, 2024

This paper presents non-intrusive reduced order models for partitioned fluid-structure interaction simulations.

Recommended citation: Azzeddine Tiba, Thibault Dairay, Florian De Vuyst, Iraj Mortazavi, Juan-Pedro Berro Ramirez. (2024). "Non-intrusive reduced order models for partitioned fluid–structure interactions." Journal of Fluids and Structures. 128, 104156.
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Machine-learning enhanced predictors for accelerated convergence of partitioned fluid-structure interaction simulations

Published in Computer Physics Communications, 2025

This paper develops machine-learning enhanced predictors to accelerate the convergence of partitioned fluid-structure interaction coupling algorithms.

Recommended citation: Azzeddine Tiba, Thibault Dairay, Florian De Vuyst, Iraj Mortazavi, Juan Pedro Berro Ramirez. (2025). "Machine-learning enhanced predictors for accelerated convergence of partitioned fluid-structure interaction simulations." Computer Physics Communications. 310, 109522.
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Machine Learning to Accelerate Fluid-Structure Interaction Simulations

Published in FSSIC 2025 Symposium Proceedings, 2025

Conference paper on machine learning methods to accelerate fluid-structure interaction simulations. Accepted at FSSIC 2025.

Recommended citation: Azzeddine Tiba, Thibault Dairay, Florian De Vuyst, Iraj Mortazavi, Juan Pedro Berro Ramirez. (2025). "Machine Learning to Accelerate Fluid-Structure Interaction Simulations." FSSIC 2025 Symposium Proceedings.

talks

CANUM

Published:

Invited talk at CANUM on nonlinear dynamical model order reduction of FSI problems.

ERCOFTAC

Published:

Invited talk at ERCOFTAC on non-intrusive reduced order modeling for fluid-structure interation simulations.

ECCOMAS

Published:

Invited talk at ECCOMAS on machine learning strategies for accelerating fluid-structure interaction simulations.

teaching