연사: 지정민 교수(University of Louisville)

일시: 2026.07.07(화) 16:00-17:00 자연과학대학 219호

초록:  Singular perturbation problems in fluid mechanics, characterized by a small (viscosity) parameter multiplying the highest-order derivatives, give rise to multiscale behavior with sharp transitions such as boundary and interior layers. These features pose significant challenges for both analysis and computation. In this talk, I will present recent developments in semi-analytic Physics-Informed Neural Network (PINN) methods designed to address these difficulties in slightly viscous fluid flows. By incorporating viscous layer correctors into the learning framework, the proposed approach significantly improves both accuracy and computational efficiency. This work highlights the synergy between asymptotic analysis and machine learning in advancing the simulation of singularly perturbed fluid equations.