Saeideh Khatiry Goharoodi
Papers
1
Total Citations
4
H-Index
1
About
Saeideh Khatiry Goharoodi is a researcher specializing in nonlinear dynamics, system identification, and evolutionary computation. Her work focuses on developing advanced sparse regression techniques to extract governing equations from experimental data, particularly for complex mechanical systems. Her most cited paper, "Evolutionary-Based Sparse Regression for the Experimental Identification of Duffing Oscillator" (4 citations), introduces a novel algorithm that combines evolutionary optimization with sparse regression to identify Coulomb friction terms in Duffing oscillators—a challenging problem in nonlinear system identification. This contribution bridges the gap between numerical simulation and real-world experimental data, offering a robust method for modeling nonlinear behaviors in engineering systems. Goharoodi's research has implications for structural health monitoring, robotics, and vibration analysis, where accurate system identification is critical. Her work demonstrates a unique integration of machine learning and physics-based modeling, positioning her as an emerging voice in the field of data-driven dynamical systems.
Research Focus
Key Achievements
Top Papers
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