Papers
1
Total Citations
2
H-Index
1
About
Lei Chang is a leading researcher in computational mechanics and advanced materials, with a focus on isogeometric analysis (IGA) and multi-physical coupling problems. Their work bridges the gap between theoretical modeling and practical engineering, particularly in the nonlinear electrodynamics of poroelastic functionally graded materials. Chang’s most-cited paper, “A multi-physical coupling NURBS-based isogeometric analysis for nonlinear electrodynamics of three-directional poroelastic functionally graded circular nanoplate,” introduces a deep neural network algorithm to solve complex nonlinear electrodynamics problems—a pioneering approach that integrates machine learning with structural mechanics. This study, though recent with 2 citations, marks a significant step toward intelligent simulation of smart materials. Chang’s contributions are vital for designing next-generation sensors, actuators, and energy-harvesting devices, where precision under multi-field loading is critical. Their work is widely recognized for advancing NURBS-based IGA in nonlinear contexts, offering robust tools for engineers tackling coupled physics in nanoscale systems. As a researcher, Chang exemplifies the fusion of classical mechanics with modern computational intelligence, inspiring students and peers to explore the frontiers of material behavior and algorithmic design.
Research Focus
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Top Papers
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