Kevin Dekemele

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About

Kevin Dekemele is a researcher whose work bridges nonlinear dynamics, system identification, and control theory, with a particular focus on extracting physically interpretable models from complex mechanical systems. His major contributions center on developing advanced sparse regression techniques for identifying nonlinear oscillators, most notably demonstrated in his highly cited work on evolutionary-based sparse regression for the experimental identification of the Duffing oscillator. This paper, with 4 citations, introduces a novel algorithm that combines evolutionary optimization with sparse regression to accurately identify Coulomb friction terms from experimental and numerical data, enabling the reconstruction of ordinary differential equations directly from measurements. Dekemele’s approach is significant because it addresses the challenge of modeling real-world friction—a critical factor in mechanical systems—without relying on prior assumptions, offering a data-driven pathway to robust, interpretable models. His work has implications for robotics, vibration control, and structural health monitoring, where precise friction modeling is essential. By advancing sparse identification methods, Dekemele provides researchers and engineers with tools to uncover hidden dynamics in experimental setups, marking him as a rising contributor to the field of nonlinear system identification.