Published in , 2026
Preprint in preparation.
Recommended citation: Azzeddine Tiba, Florian de Vuyst, Iraj Mortazavi. Online adaptive non-intrusive model reduction via manifold interpolation and subspace updates: application to FSI convergence acceleration. 2026.
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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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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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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.
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.