Georgia Institute of Technology College of Sciences

Hyperbolic conservation laws are fundamental to modeling wave propagation, fluid dynamics, and collective behavior across science and engineering. Despite their universal importance, traditional numerical solvers rely on explicitly known flux functions and parameters, which are often unavailable in realistic scenarios where only trajectory or observation data are accessible. In this talk, I will discuss our recent work in data-driven approaches for learning hyperbolic conservation laws that preserve their intrinsic physical and mathematical structures. These developments combine principles from numerical analysis, partial differential equations, and machine learning to construct models that are both predictive and faithful to the underlying conservation and stability properties of the governing equations.