This minisymposium explores the integration of Machine-Learning (ML) based Model Order Reduction (MOR) methods into complex 3D Finite Element models for biomechanical applications, with the aim of constructing efficient Digital Twin Models. These models enable rapid computational simulations, which are critical for tasks such as model calibration, scientific analysis, and pre-surgical planning. Patient-specific computational modeling has evolved considerably, playing a pivotal role in biomechanics and mechanobiology. These models are now capable of predicting patient-specific therapeutic outcomes and mechanistic responses, yet they often require large-scale simulations that are computationally prohibitive for real-time application. MOR techniques are essential in addressing these computational challenges by systematically reducing the model's degrees of freedom while maintaining essential accuracy. This approach allows for the practical use of high-fidelity simulations on standard computational platforms.

This session will discuss the latest methodological advancements in MOR, focusing on the incorporation of ML techniques to enhance model efficiency and accuracy. Topics will include the challenges of preserving model fidelity while reducing computational complexity, as well as the implementation of MOR in multiscale modeling frameworks that capture both macroscopic and microscopic biomechanical phenomena. Participants will engage with recent developments in the application of MOR to biomechanical modeling, particularly in the context of Digital Twin construction. The discussions will center on the theoretical and practical aspects of balancing model accuracy with computational feasibility, a critical consideration for advancing biomechanical research and its applications in clinical practice.