Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques.

Abstract

The complexities of bone architecture, with its hierarchical organization and varying spatiotemporal scales, necessitate advanced modeling techniques to capture its mechanical behavior precisely. This review aims to highlight recent trends in capturing the multiscale nature of bone using two primary computational approaches: classical and data-driven frameworks. Each class is assessed regarding its versatility in achieving scale dimensions, modeling complex behavior, integrating biological data, and balancing computational efficiency and interpretability. In addition, hybrid techniques have been shown to offer future avenues for promising robust and generalizable modeling. Therefore, particular attention has been given to the synergy between these techniques. A hierarchical decision matrix is proposed to translate this review into actionable guidance, shedding light on the selection or combination of appropriate techniques based on specific application contexts, such as data availability, modeling objectives, and computational constraints. This review aims to serve as both a state-of-the-art synthesis and a practical reference for future advancements in multiscale bone biomechanics.