We are committed to advancing fundamental science and high-impact processing technologies that enable breakthrough materials and predictive processes for next-generation semiconductor manufacturing.
Chaotic Advection & Nonlinear Fluid Dynamics
We pursue first-principles research in theoretical fluid dynamics, focusing on chaotic advection, Lagrangian transport, and nonlinear flow instability.
By identifying coherent structures, transport barriers, and strange attractors, we establish a fundamental theoretical framework for understanding mixing, transport, and reaction in complex flows.
Data-Driven Modeling & Machine Learning
We believe data is not a replacement for theory, but a lens to reveal it.
Our research combines physics-based computation and machine learning to extract governing patterns, reduced representations, and surrogate dynamics, enabling interpretable and predictive modeling of complex material and flow systems.
Spectral Element Method–Based High-Accuracy CFD
We develop high-order computational fluid dynamics grounded in numerical analysis and continuum mechanics. By advancing the Spectral Element Method, our research combines geometric flexibility with spectral-level accuracy, enabling the faithful resolution of multiscale transport phenomena, sharp interfaces, and nonlinear flow dynamics in complex systems.
