Parametric Hyperbolic Conservation Laws: A Unified Framework for Conservation, Entropy Stability, and Hyperbolicity

TL;DR

This paper introduces SymCLaw, a unified data-driven framework for hyperbolic conservation laws that guarantees conservation, entropy stability, and hyperbolicity, enabling accurate and stable long-term predictions across various physical systems.

Contribution

It presents a novel parametric approach that enforces conservation, entropy stability, and hyperbolicity simultaneously directly from data, unlike prior methods.

Findings

Successfully models benchmark hyperbolic systems including Euler and shallow water equations.

Generalizes well to unseen initial conditions and noisy data.

Achieves stable, accurate long-time predictions.

Abstract

We propose a parametric hyperbolic conservation law (SymCLaw) for learning hyperbolic systems directly from data while ensuring conservation, entropy stability, and hyperbolicity by design. Unlike existing approaches that typically enforce only conservation or rely on prior knowledge of the governing equations, our method parameterizes the flux functions in a form that guarantees real eigenvalues and complete eigenvectors of the flux Jacobian, thereby preserving hyperbolicity. At the same time, we embed entropy-stable design principles by jointly learning a convex entropy function and its associated flux potential, ensuring entropy dissipation and the selection of physically admissible weak solutions. A corresponding entropy-stable numerical flux scheme provides compatibility with standard discretizations, allowing seamless integration into classical solvers. Numerical experiments on benchmark problems, including Burgers, shallow water, Euler, and KPP equations, demonstrate that SymCLaw generalizes to unseen initial conditions, maintains stability under noisy training data, and achieves accurate long-time predictions, highlighting its potential as a principled foundation for data-driven modeling of hyperbolic conservation laws.