How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Entropy, invariant-domain and weak-limit hypotheses
Remark
The relevant numerical properties have separate hypotheses. A shared-face finite-volume update on a periodic uniform grid with cells of common width uses the same ratio in every cell, where . Its balance is with one vector flux per face shared by the two neighboring cells. Finite telescoping conserves , and hence the total vector . That algebra alone gives no membership of the updated states in an admissible set, entropy inequality, or convergence result.
For the symmetric ideal-gas HLL/LLF branch, the stated one-step positivity result assumes and at every face. For a general HLL middle branch, the convex invariant-domain result uses a different explicit hypothesis. Let be a convex state set, meaning every segment joining two members lies in , and write for the time-step-to-cell-width ratio in this HLL formula. The approximate HLL fan at a face with has middle state This is the state in the two-wave approximation; its associated model flux is not assumed to be the physical flux . The fan is to the left of , between and , and to the right of . Its spatial averages over the left and right adjacent half-cells at time , respectively, are under the one-sided bounds and . The HLL invariant-domain result assumes both half-cell averages lie in at every face and every cell mean lies in . Since each cell update is the average of its two adjacent half-cell averages, convexity then gives the updated mean in . A sufficient way to verify the fan-average hypothesis is to show ; the result does not infer from supplied endpoint speed estimates alone.
The sketch fixes the fan and half-cell conventions used in those formulas. The horizontal axis is , with at the face. The vertical arrow at the left marks increasing time and is offset for readability; the label marks the face at . The wave lines have supplied speeds , with their slopes representing . The dashed verticals mark the adjacent cell centers at and . The one-sided CFL bounds keep each wave inside its half-cell through time . The proof and averages are the displayed algebraic formulas, not the drawing.
\begin{tikzpicture}[x=2cm,y=1.15cm]
\draw[->] (-1.35,0) -- (1.45,0) node[right] {$x$};
\draw[->] (-1.3,0) -- (-1.3,1.2) node[above] {$t$};
\draw[dashed] (-0.5,0) -- (-0.5,1.05);
\draw[dashed] (0.5,0) -- (0.5,1.05);
\draw[thick] (0,0) -- (-0.34,1);
\draw[thick] (0,0) -- (0.34,1);
\node at (-0.95,0.7) {$U_L$};
\node at (0,0.68) {$U_*$};
\node at (0.95,0.7) {$U_R$};
\node[below] at (-0.5,0) {$-h/2$};
\node[below] at (0.5,0) {$h/2$};
\node[above right] at (-0.34,1) {$S_L$};
\node[above left] at (0.34,1) {$S_R$};
\node[below right] at (0,0) {$0$};
\node[left] at (-1.3,1) {$\Delta t$};
\end{tikzpicture}
The entropy-stable dissipative-flux definition specifies a consistent entropy-conservative flux with positive-semidefinite entropy-variable dissipation, and explicitly supplies no fully discrete time-step inequality. Any semidiscrete entropy inequality for a selected semidiscrete evolution is a separate result; a fully discrete bound also requires an argument for the chosen time integrator. The ratio above is only the mesh time-step to width ratio and is not physical viscosity.
For the convex-scaling limiter, assume additionally that is closed, meaning it contains every limit in of a convergent sequence of its members; remains convex. The limiter applies to a finite nonempty set of node states with positive weights summing to one and an already admissible mean . It returns the largest for which all scaled states belong to . The feasible parameter set is closed in and contains , so this maximum exists. Direct summation gives Thus the construction preserves the supplied mean and constrains the chosen nodes; it has no admissibility branch for a mean outside and gives no pointwise-in-cell assertion between those nodes.
The conservative finite-volume weak-limit theorem is a separate scalar one-dimensional result on . It assumes , a continuous scalar flux , initial data , a continuous consistent numerical flux , initial cell averages, , and the Axiom of Countable Choice (for every family of nonempty sets, there is a function with for every ). Strong local convergence of the reconstructed states and local uniform bounds on every bounded space-time cylinder through are also hypotheses. Under them the limit is a locally bounded scalar distributional weak solution. In its proof, is used only in the local spatial-translation step of the face-flux convergence lemma; the discrete summation and limit passage require no further choice. The theorem assumes neither a CFL condition nor an entropy inequality, proves neither convergence nor compactness, and does not apply to the compressible Euler system.
Remark references
Convex invariant domains for a one-step update — defines convexity as closure under line segments and states the one-step invariant-domain scope.
Shared-face flux telescoping conservation — proves periodic total-vector conservation by exact shared-face cancellation.
Symmetric HLL speeds preserve ideal-gas density and internal-energy positivity — supplies the symmetric ideal-gas branch and its endpoint speed and CFL hypotheses.
First-order HLL updates are averages of adjacent half-cell fan averages — defines the HLL intermediate state and exact adjacent half-cell average formulas.
First-order HLL update preserves a convex invariant domain under admissible fan averages — states the general convex-set result under the fan-average hypothesis.
Entropy-stable flux from positive-semidefinite entropy-variable dissipation — defines the entropy-variable dissipation flux and expressly excludes a fully discrete time-step property.
Maximal convex scaling toward an admissible cell mean — states the admissible-mean precondition and finite-node scaling output.
Conservative finite-volume strong-limit theorem — supplies the exact scalar weak-limit conclusion and its strong-convergence, local-bound, and no-CFL/no-entropy scope.
The Axiom of Countable Choice () — defines as a choice function for every countable family of nonempty sets.
De Sterck and Ullrich, §§3.4.1–3.4.3 — full course notes treatment of finite-volume conservation and scalar-law context.
Zhang and Shu, §2 — full article treatment of Euler positivity, convex combinations, and finite-node limiting.
Tadmor, §§4.1–4.3 and 5 — full chapter treatment of entropy-variable dissipation and fully discrete analysis.
Ketcheson, LeVeque, and del Razo, Euler approximate-solver chapter — full chapter treatment of the HLL fan and supplied speeds.
Depends on
Maximal convex scaling toward an admissible cell mean
The Axiom of Countable Choice ()
Convex invariant domains for a one-step update
Entropy-stable flux from positive-semidefinite entropy-variable dissipation
First-order HLL updates are averages of adjacent half-cell fan averages
Shared-face flux telescoping conservation
Conservative finite-volume strong-limit theorem
First-order HLL update preserves a convex invariant domain under admissible fan averages
Symmetric HLL speeds preserve ideal-gas density and internal-energy positivity
Dependency tree · 2 levels
Sources
- H. De Sterck and P. Ullrich, Introduction to Computational PDEs, Chapter 3
- Xiangxiong Zhang and Chi-Wang Shu, On Positivity-Preserving High Order Discontinuous Galerkin Schemes for Compressible Euler Equations on Rectangular Meshes
- E. Tadmor, Entropy Stable Schemes for Hyperbolic Conservation Laws, Handbook of Numerical Methods for Hyperbolic Problems, Vol. 17
- D. I. Ketcheson, R. J. LeVeque, and M. J. del Razo, Riemann Problems and Jupyter Solutions