S. C. Katsoudas et al., “Hemodynamic Shear Stress Patterns in Abdominal Aortic Aneurysms and Healthy Aortas: A CFD Study,” Cardiovasc Eng Tech, Feb. 2026, doi: 10.1007/s13239-026-00823-3.
S. C. Katsoudas et al., “Linking aneurysmal geometry and hemodynamics using computational fluid dynamics,” Physics of Fluids, vol. 38, no. 3, p. 031910, Mar. 2026, doi: 10.1063/5.0315857.
S. Malatos et al., “Comparison of Hemodynamics After Fenestrated, Branched, and Chimney Endovascular Aneurysm Repair Employing Computational Fluid Dynamics,” Journal of Clinical Medicine, vol. 15, no. 5, Mar. 2026, doi: 10.3390/jcm15051914.
P. Korkidis et al., “Ensemble and Evolutionary Fuzzy Classifier Systems for Abdominal Aortic Aneurysms,” Algorithms, vol. 19, no. 2, p. 103, Jan. 2026, doi: 10.3390/a19020103.
K.-E. Aslani, I. E. Sarris, and E. Tzirtzilakis, “Micromagnetorotation effects in micropolar magnetohydrodynamic blood flow through stenosis,” Physics of Fluids, vol. 38, no. 2, p. 021906, Feb. 2026, doi: 10.1063/5.0309533.
Κ.-E. Aslani, I. E. Sarris, and E. Tzirtzilakis, “On the development of OpenFOAM solvers for simulating MHD micropolar fluid flows with or without the effect of micromagnetorotation,” Computer Physics Communications, vol. 324, p. 110144, Jul. 2026, doi: 10.1016/j.cpc.2026.110144.
K. Kyriakoudi, S. Katsoudas, G. Chrimatopoulos, M. Xenos, and E. Tzirtzilakis, “Simulation of blood flow in pathological vessels: a finite volume approach,” presented at the 1st Hellenic-Italian Conference on Computational Mechanics, Biomechanics and Mechanics of Materials (HICOMP), Jun. 2025. [Online]. Available: https://hicomp2025.org/
S. Katsoudas, T. Aravanis, M. Xenos, and E. Tzirtzilakis, “Predictive modeling of blood flow in stenotic geometries using CFD and machine learning,” presented at the 1st Hellenic-Italian Conference on Computational Mechanics, Biomechanics and Mechanics of Materials (HICOMP), Jun. 2025. [Online]. Available: https://hicomp2025.org/
S. Katsoudas, S. Malatos, A. Raptis, M. Matsagkas, A. Giannoukas, and M. Xenos, “Blood flow simulation in bifurcating arteries: a multiscale approach after fenestrated and branched endovascular aneurysm repair,” Mathematics, vol. 13, no. 9, p. 1362, Apr. 2025, doi: 10.3390/math13091362.
D. Arampatzis et al., “A comparative study of unsupervised and deep learning methods for automatic segmentation of abdominal aortic aneurysm on CT Images: Preliminary results,” in GeNeDIS 2024, P. Vlamos, Ed., Cham: Springer Nature Switzerland, 2025, pp. 335–345. doi: 10.1007/978-3-032-03394-9_33.
T. P. Vagenas et al., “Self-supervised pre-training with intensity guided masking for enhanced aorta segmentation in CT,” in 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2025, pp. 1–7. doi: 10.1109/EMBC58623.2025.11253827.
G. E. Stavroulakis, A. D. Mouratidou, and G. A. Drosopoulos, “Multiscale Finite Elements Using Neural Network Material Metamodels,” in Multiscale and Multiphysics Modelling for Advanced and Sustainable Materials: Euromech Colloquium 642, P. Trovalusci, T. Sadowski, and A. Ibrahimbegovic, Eds., Cham: Springer Nature Switzerland, 2025, pp. 363–375. doi: 10.1007/978-3-031-84379-2_27.
N. Kladovasilakis et al., “FEM Modelling and Digital Twins of Aorta Aneurysms,” presented at the 1st Hellenic-Italian Conference on Computational Mechanics, Biomechanics and Mechanics of Materials (HICOMP), Jun. 2025. [Online]. Available: https://hicomp2025.org/files/uploads/general/HICOMP_2025_programme.pdf
T. Aravanis, G. Chrimatopoulos, M. Xenos, and E. Tzirtzilakis, “Machine Learning-Based Geometric Interpolation for Fluid-Flow Modelling,” in Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics, in PCI ’24. New York, NY, USA: Association for Computing Machinery, May 2025, pp. 407–412. doi: 10.1145/3716554.3716616.
C. Manopoulos, A. Raptis, D. Koutsouris, and G. Matsopoulos, “SAFE-AORTA: AI-enabled clinical decision support system for the abdominal aortic aneurysm disease,” presented at the European Symposium on Biomedical Engineering – ESBME, May 2025.
D. Petrolekas, A. Raptis, E. Karavasilis, K. Moulakakis, I. Kakisis, and C. Manopoulos, “4D flow MRI-enabled patient-specific computational hemodynamics of thoracic aorta: CFD predictions vs. in vivo imaging data,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. [Online]. Available: https://doi.org/10.1177/14604582251347140
M. Athanasiou, “Exploring state-of-the-art deep learning architectures for blood flow prediction in pathological vessels,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. doi: https://doi.org/10.1177/14604582251347140.
P. Sarantides, “Structural modelling and simulation of abdominal aortic aneurysms accounting for intraluminal thrombus and calcifications,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. [Online]. Available: https://doi.org/10.1177/14604582251347140
E. Papanikolaou, D. Sokolis, D. Iliopoulos, and C. Manopoulos, “Dissection mechanics of tissue components in human aorta,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. [Online]. Available: https://doi.org/10.1177/14604582251347140
K. Kyparissis et al., “Numerical evaluation of abdominal aortic aneurysms utilizing finite element method,” Diagnostics, vol. 15, no. 6, p. 697, Mar. 2025, doi: 10.3390/diagnostics15060697.
A. D. Mouratidou and G. E. Stavroulakis, “A Neural Network Constitutive Model and Automatic Stiffness Evaluation for Multiscale Finite Elements,” Applied Sciences, vol. 15, no. 7, p. 3697, Jan. 2025, doi: 10.3390/app15073697.
E. Bei, K. Politof, K. Moirogiorgou, M. Antonakakis, and M. Zervakis, “Morphological analysis of intraluminal thrombus of abdominal aortic aneurysm,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. [Online]. Available: https://doi.org/10.1177/14604582251347140
A. Mouratidou, C.-F. Xydias, N. Kladovasilakis, and G. E. Stavroulakis, “FEM Modelling of Aorta Aneurysms in View of Virtual Sensor Development and Rupture Estimate,” in 2025 IEEE Medical Measurements & Applications (MeMeA), May 2025, pp. 1–6. doi: 10.1109/MeMeA65319.2025.11068029.
D. Arampatzis et al., “A preliminary comparison of unsupervised and deep learning approaches for automatic abdominal aortic aneurysm segmentation on CT images,” presented at the 11th Panhellenic Conference of Biomedical Technology, May 2025. [Online]. Available: https://doi.org/10.1177/14604582251347140
A. D. Mouratidou, G. A. Drosopoulos, and G. E. Stavroulakis, “Ensemble of physics-informed neural networks for solving plane elasticity problems with examples,” Acta Mech, vol. 235, no. 11, pp. 6703–6722, Nov. 2024, doi: 10.1007/s00707-024-04053-3.
T. Aravanis, G. Chrimatopoulos, M. Xenos, and E. Tzirtzilakis, “Forecasting two-dimensional channel flow using machine learning,” Physics of Fluids, vol. 36, no. 10, p. 103617, Oct. 2024, doi: 10.1063/5.0231005.
P. Sarantides, A. Raptis, D. Mathioulakis, K. Moulakakis, J. Kakisis, and C. Manopoulos, “Computational study of abdominal aortic aneurysm walls accounting for patient-specific non-uniform intraluminal thrombus thickness and distinct material models: a pre- and post-rupture case,” Bioengineering, vol. 11, no. 2, Art. no. 2, Feb. 2024, doi: 10.3390/bioengineering11020144.
C. Mavridis et al., “Automatic Segmentation in 3D CT Images: A Comparative Study of Deep Learning Architectures for the Automatic Segmentation of the Abdominal Aorta,” Electronics, vol. 13, no. 24, p. 4919, Jan. 2024, doi: 10.3390/electronics13244919.
T. Aravanis, G. Chrimatopoulos, M. Xenos, and E. Tzirtzilakis, “Machine Learning-Based Geometric Interpolation for Fluid-Flow Modelling,” in Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics, Athens Greece: ACM, Dec. 2024, pp. 407–412. doi: 10.1145/3716554.3716616.
P. N. Smyrlis et al., “Aorta localization in Computed Tomography images: A YoloV9 segmentation approach,” in 2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Athens, Greece: IEEE, Sep. 2024, pp. 163–167. doi: 10.1109/SEEDA-CECNSM63478.2024.00037.
T. Aravanis, G. Chrimatopoulos, M. Xenos, and E. E. Tzirtzilakis, “Machine-Learning Modelling of a Fluid Flow in a Two-Dimensional Channel,” in 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA), Chania Crete, Greece: IEEE, Jul. 2024, pp. 1–8. doi: 10.1109/IISA62523.2024.10786684.
G. S. Stavroulakis, A. Mouratidou, and G. D. Drosopoulos, “Nonlinear interaction in composites using physics informed neural networks,” in 16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, Jul. 2024. doi: 10.23967/wccm.2024.074.
P. Sarantides et al., “Image segmentation, modeling and computational structural analysis of patient-specific abdominal aortic aneurysms including intraluminal thrombus through a semi-automated workflow,” in 11th International Conference from “Scientific Computing to Computational Engineering” (IC-SCCE), 2024.
