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The rapid expansion of data availability in engineering disciplines has catalyzed the integration of machine learning (ML) to tackle complex problems. Despite the transformative potential of ML, its opaque nature poses significant challenges for engineers. This paper presents the development of empirical indices to bridge the gap between ML-driven insights and traditional engineering theory/domain knowledge by converting high-dimensional ML insights into interpretable, low-dimensional indices that retain predictive rigor.

International Research Awards on Science Health and Engineering

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