Jiawei Qian
Papers
2
Total Citations
33
H-Index
2
About
Jiawei Qian is a researcher at the forefront of data-driven dynamical systems, with a focus on uncovering the hidden physics of complex, multistable, and hysteretic behaviors. His work bridges the gap between traditional nonlinear dynamics and modern machine learning, developing innovative methods to reconstruct and model systems that exhibit memory and multiple stable states. Qian’s most influential paper, "A data-driven reconstruction method for dynamic systems with multistable property" (2022), has garnered 21 citations, establishing a foundational approach for extracting governing equations from noisy, multistable data. Building on this, his 2024 study "Discovering differential governing equations of hysteresis dynamic systems by data-driven sparse regression method" (12 citations) extends these techniques to hysteresis—a notoriously challenging phenomenon in materials science and engineering. By combining sparse regression with physical principles, Qian enables the automatic discovery of interpretable models from measurements, offering a powerful tool for applications ranging from smart materials to energy harvesting. His work stands out for its clarity and practical impact, making him a rising voice in the field of scientific machine learning.
Research Focus
Key Achievements
Top Papers
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