Add ES-DG optimized 3S* low-storage RK methods (Al Jahdali et al. 2022)#3241
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singhharsh1708 wants to merge 1 commit intoSciML:masterfrom
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Add ES-DG optimized 3S* low-storage RK methods (Al Jahdali et al. 2022)#3241singhharsh1708 wants to merge 1 commit intoSciML:masterfrom
singhharsh1708 wants to merge 1 commit intoSciML:masterfrom
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- Implement 16 new methods (advection- and vortex-optimized) - Reuse existing LowStorageRK3S cache structures (no allocations) - Add tableau constructors and alg_cache implementations - Register methods in alg_utils and exports - All tests pass (517 + JET + Aqua)
Author
ChrisRackauckas
approved these changes
Mar 26, 2026
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📌 Description
This PR adds 16 new 3S* low-storage explicit Runge–Kutta methods based on the work of Al Jahdali et al. (2022), designed for entropy-stable discontinuous Galerkin (ES-DG) discretizations.
The implemented methods include both:
Each method follows the existing 3S* low-storage formulation and integrates seamlessly with the current
OrdinaryDiffEqLowStorageRKinfrastructure.✨ What’s included
AlJahdaliAdv3S*(advection-optimized)AlJahdaliVor3S*(vortex-optimized)algorithms.jllow_storage_rk_caches.jlalg_utils.jlOrdinaryDiffEqLowStorageRK.jl(exports)🧠 Implementation Details
LowStorageRK3SConstantCacheLowStorageRK3SCacheperform_step!for 3S* methods (no changes required)🧪 Testing
No regressions observed.
✅ Checklist
🔗 Additional context
Implements methods discussed in issue #2903.
These methods extend the solver suite with problem-specific optimized integrators for high-order DG discretizations, particularly useful in compressible flow and wave propagation problems.