Application of computational biomechanical models in analyzing the impact of human capital mobility on economic growth

This paper innovatively applies computational biomechanical models to the field of human capital flow research, establishing a novel analytical framework. By introducing the potential field concept from biomechanics to describe economic development dynamics, employing continuum mechanics methods to characterize talent flow patterns, and integrating numerical computation techniques, we achieved systematic simulation of the relationship between human capital flow and economic growth. The research reveals that human capital flow promotes economic growth through three primary mechanisms: knowledge accumulation effect, innovation-driven effect, and industrial upgrading effect. In the short term, human capital flow can contribute to a 1.35 percentage point increase in GDP growth within one year; in the long term, its total contribution to economic growth rises from 3.19% to 7.42% over a decade. The study identifies four flow patterns: agglomeration, gradient, network, and circular, with agglomeration-type flow showing the most significant economic effect, contributing 42.5% to economic growth. Policy simulation results indicate that innovation-driven strategies can drive GDP growth by 2.85 percentage points, industrial upgrading strategies contribute 2.42 percentage points, talent incentive strategies achieve 2.15 percentage points growth, while comprehensive optimization strategies can realize a 3.65 percentage point growth effect. Based on these findings, we propose policy recommendations including building a multi-level talent support system, implementing a “gradient cultivation, collaborative development” regional development strategy, and following the principle of “top-level design, phased implementation, key breakthrough.” This research not only achieves methodological innovation but also provides a theoretical foundation and practical guidance for formulating scientific talent policies.