A Neural-Network Approach for the Stability of Loosely-Coupled FSI Simulations in Hemodynamics
Abstract
The development of accurate and efficient computational methods for fluid-structure interaction (FSI) problems remains an active research area due to its broad engineering and biomedical applications. Partitioned loosely coupled (LC) schemes are attractive for large-scale simulations because of their modularity and low computational cost. However, their applicability is limited by stability issues in high added-mass regimes, where fluid and structure densities are comparable, as in hemodynamics. In our recent work, we proposed an LC algorithm based on Dirichlet-Neumann interface coupling, the LC-DN-α scheme, which improves the stability of the coupling through a stabilization parameter α. Although theoretical stability has been demonstrated under suitable constraints on α, its optimal value depends on the physical and numerical characteristics of the FSI problem and is difficult to determine in practice. In this work, we propose a data-driven strategy to automatically estimate the optimal value of α. We develop a feed-forward neural network to predict α from the descriptors of the FSI problem. The model is trained on a dataset of baseline FSI problems in large added-mass regimes and evaluated on more realistic configurations to assess robustness and generalization. Numerical results show that the proposed model generalizes across the considered FSI scenarios and provides robust estimates of α under moderate variations in the problem setup, including changes in geometry and loading conditions, that do not completely alter the interaction mechanics represented in the training set. The methodology is intentionally developed and validated on simplified FSI problems, providing a proof of concept for data-driven strategies to improve the efficiency of partitioned FSI simulations and supporting their extension to more complex and realistic scenarios.
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BibTeX
@misc{renzi2026neuralnetwork,
title = {{A Neural-Network Approach for the Stability of Loosely-Coupled FSI Simulations in Hemodynamics}},
author = {Francesca Renzi and Stefano Pagani and Paola F. Antonietti and Christian Vergara},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10943},
eprint = {2610.10943},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10943},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Neural-Network Approach for the Stability of Loosely-Coupled FSI Simulations in Hemodynamics
AU - Francesca Renzi
AU - Stefano Pagani
AU - Paola F. Antonietti
AU - Christian Vergara
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10943
UR - https://arxiv.org/abs/2610.10943
AB - The development of accurate and efficient computational methods for fluid-structure interaction (FSI) problems remains an active research area due to its broad engineering and biomedical applications. Partitioned loosely coupled (LC) schemes are attractive for large-scale simulations because of their modularity and low computational cost. However, their applicability is limited by stability issues in high added-mass regimes, where fluid and structure densities are comparable, as in hemodynamics. In our recent work, we proposed an LC algorithm based on Dirichlet-Neumann interface coupling, the LC-DN-α scheme, which improves the stability of the coupling through a stabilization parameter α. Although theoretical stability has been demonstrated under suitable constraints on α, its optimal value depends on the physical and numerical characteristics of the FSI problem and is difficult to determine in practice. In this work, we propose a data-driven strategy to automatically estimate the optimal value of α. We develop a feed-forward neural network to predict α from the descriptors of the FSI problem. The model is trained on a dataset of baseline FSI problems in large added-mass regimes and evaluated on more realistic configurations to assess robustness and generalization. Numerical results show that the proposed model generalizes across the considered FSI scenarios and provides robust estimates of α under moderate variations in the problem setup, including changes in geometry and loading conditions, that do not completely alter the interaction mechanics represented in the training set. The methodology is intentionally developed and validated on simplified FSI problems, providing a proof of concept for data-driven strategies to improve the efficiency of partitioned FSI simulations and supporting their extension to more complex and realistic scenarios.
ER -