Georgia Institute of Technology College of Sciences

This talk presents two recent advances in Neural Network with Local Converging Inputs (NNLCI) —a novel surrogate model for efficiently resolving nonlinear flow dynamics at modest computational cost

First, a powerful and efficient technique is introduced to extend NNLCI to unstructured computational fluid dynamics. The framework is validated on two-dimensional inviscid supersonic flow in channels with varying bump geometries and positions. The NNLCI model accurately captures key flowfield structures and dynamics, including regions with highly nonlinear shock interactions while achieving a speedup of more than two orders of magnitude.

Second, we conduct a comprehensive benchmark study to compare our method with current state-of-the-art AI-based PDE solvers. Across representative hyperbolic conservation law problems, NNLCI consistently deliver superior accuracy, efficiency and robustness in resolving challenging sharp discontinuities and wave interactions. The work provides practical guidance for model selection in scientific machine learning applications