A Positivity-Preserving Relaxation Algorithm
This work addresses the need for stable and physically consistent numerical methods in computational science, particularly for applications involving ordinary and partial differential equations, though it appears incremental as it builds on existing Patankar-type and relaxation frameworks.
The paper tackled the problem of ensuring positivity and conservation of functionals like entropy or energy in numerical approximations for differential equations, by combining Patankar-type methods with relaxation procedures, resulting in unconditionally positive and conservative solutions validated through numerical tests.
We combine Patankar-type methods with suitable relaxation procedures that are capable of ensuring correct dissipation or conservation of functionals such as entropy or energy while producing unconditionally positive and conservative approximations. To that end, we adapt the relaxation algorithm to enforce positivity by using either ideas from the dense output framework when a linear invariant must be preserved, or simply a geometric mean if the only constraint is positivity preservation. The latter merely requires the solution of a scalar nonlinear equation while former results in a coupled linear-nonlinear system of equations. We present sufficient conditions for the solvability of the respective equations. Several applications in the context of ordinary and partial differential equations are presented, and the theoretical findings are validated numerically.