Topic description

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The core objective of this PhD is to transition from high-fidelity, computationally intensive multiscale finite element models to low-fidelity, real-time digital twins. By integrating physics-based equations with machine learning, this research aims to create scalable models that maintain high predictive accuracy for clinical deployment. This task builds on strong prior work, including real-time strain localization using dictionary-based ROM-nets (Rohan et al., ) developed by an expert in reduced-order modeling and expert in biomechanical modeling.

The main contribution of this PhD is the...