Lecturers and Sessions Description

Suggested readings

Avril, S. (2017). Hyperelasticity of soft tissues and related inverse problems. Material parameter identification and inverse problems in soft tissue biomechanics, 37-66.

Bertoglio, C., Moireau, P., & Gerbeau, J. F. (2012). Sequential parameter estimation for fluid–structure problems: application to hemodynamics. International Journal for Numerical Methods in Biomedical Engineering, 28(4), 434-455.

Bisighini, B., Aguirre, M., Biancolini, M. E., Trovalusci, F., Perrin, D., Avril, S., & Pierrat, B. (2023). Machine learning and reduced order modelling for the simulation of braided stent deployment. Frontiers in physiology, 14, 1148540.

Camps, J., Lawson, B., Drovandi, C., Minchole, A., Wang, Z. J., Grau, V., ... & Rodriguez, B. (2021). Inference of ventricular activation properties from non-invasive electrocardiography. Medical Image Analysis, 73, 102143.

Díez, P., Muixí, A., Zlotnik, S., & García‐González, A. (2021). Nonlinear dimensionality reduction for parametric problems: A kernel proper orthogonal decomposition. International Journal for Numerical Methods in Engineering, 122(24), 7306-7327.

Fresca, S., Dede’, L., & Manzoni, A. (2021). A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs. Journal of Scientific Computing, 87, 1-36.

Fresca, S., & Manzoni, A. (2022). POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Computer Methods in Applied Mechanics and Engineering, 388, 114181.

de Chou, R. S., Sinclair, M., Lynch, S., Xiao, N., Najman, L., Vignon-clementel, I., & Talbot, H. (2024). Finite Volume Informed Graph Neural Network for Myocardial Perfusion Simulation. In Medical Imaging with Deep Learning.

Martonová, D., Peirlinck, M., Linka, K., Holzapfel, G. A., Leyendecker, S., & Kuhl, E. (2024). Automated model discovery for human cardiac tissue: Discovering the best model and parameters. Computer Methods in Applied Mechanics and Engineering, 428, 117078.

Nolte, D., & Bertoglio, C. (2022). Inverse problems in blood flow modeling: A review. International journal for numerical methods in biomedical engineering, 38(8), e3613.

Peirlinck, M., Linka, K., Hurtado, J. A., & Kuhl, E. (2024). On automated model discovery and a universal material subroutine for hyperelastic materials. Computer Methods in Applied Mechanics and Engineering, 418, 116534.

Vignon‐Clementel, I. E., & Pant, S. (2022). Patient‐specific Hemodynamic Simulations: Model Parameterization from Clinical Data to Enable Intervention Planning. Biological Flow in Large Vessels: Dialog Between Numerical Modeling and In Vitro/In Vivo Experiments, 139-161.