A moment approach for entropy solutions of parameter-dependent hyperbolic conservation laws
Cl\'ement Cardoen, Swann Marx, Anthony Nouy, Nicolas Seguin

TL;DR
This paper introduces a moment-based numerical method for solving parameter-dependent hyperbolic PDEs using a hierarchy of convex optimization problems, enabling accurate solution reconstruction and uncertainty quantification.
Contribution
It develops a novel approach combining parametric entropy measure-valued solutions with semidefinite programming to approximate solutions of hyperbolic PDEs with convergence guarantees.
Findings
Convergent sequence of moment approximations for entropy MV solutions.
Ability to reconstruct solution graphs using Christoffel-Darboux kernels.
Effective uncertainty quantification for parametrized PDEs.
Abstract
We propose a numerical method to solve parameter-dependent hyperbolic partial differential equations (PDEs) with a moment approach, based on a previous work from Marx et al. (2020). This approach relies on a very weak notion of solution of nonlinear equations, namely parametric entropy measure-valued (MV) solutions, satisfying linear equations in the space of Borel measures. The infinite-dimensional linear problem is approximated by a hierarchy of convex, finite-dimensional, semidefinite programming problems, called Lasserre's hierarchy. This gives us a sequence of approximations of the moments of the occupation measure associated with the parametric entropy MV solution, which is proved to converge. In the end, several post-treatments can be performed from this approximate moments sequence. In particular, the graph of the solution can be reconstructed from an optimization of the…
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Taxonomy
TopicsModel Reduction and Neural Networks · Fluid Dynamics and Turbulent Flows
