(Bio)-Mechanics

This paper establishes machine learning as a high-speed surrogate for computationally expensive finite element (FE) simulations in biomechanical implant analysis. Using a large parametric FE dataset (10,000+ simulations), the authors train multiple models and show that Random Forest achieves near-perfect predictive accuracy (R² ≈ 0.97–0.99) in predicting bone strain under varying implant designs, materials, and physiological conditions. The central contribution is demonstrating that ML can reduce simulation time from hours to seconds while preserving accuracy, enabling rapid design optimization and patient-specific implant evaluation in orthopedic applications.