Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
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.

A nodal curl-free mode outside the compatible gradient image

Example

On one tetrahedron, compare the naive vector nodal space [P1(K)]3 with the compatible scalar-to-edge gradient map ∇Wh0 of Lowest-order Whitney and Nedelec de Rham spaces. The vector field v(x)=x1e1 lies in the nodal vector space, has zero curl, and is not the gradient of a continuous piecewise-affine scalar. Thus the naive nodal curl-curl matrix has a zero-energy mode outside that compatible gradient image. This identifies a discrete kernel mismatch; it does not claim that gradients are absent from the continuum curl kernel.

Facts & Assumptions

Given: Countable Choice for the real L2 gauge convention; a reference tetrahedron, the scalar nodal space and compatible edge space of Lowest-order Whitney and Nedelec de Rham spaces, and the gauge formulation of Kernel quotient and gauge for discrete curl-curl.

[A1]

Countable Choice is assumed by the real L2 product and the stable gauge supplier; no full Axiom of Choice is used. (The Axiom of Countable Choice (ACω))

[F1]

The scalar nodal space consists of affine functions on the tetrahedron, so every member has constant gradient. (Lowest-order Whitney and Nedelec de Rham spaces)

[F2]

The pure curl-curl energy vanishes on its curl kernel, and the mixed gauge formulation fixes a representative by orthogonality to that kernel. (Kernel quotient and gauge for discrete curl-curl, Stable mixed curl-curl formulation with kernel gauge)

Verification

1.1A1givenF2algebra

The field v(x)=(x1,0,0) is vector-valued affine, so it belongs to [P1(K)]3. Its componentwise curl is zero because its only nonzero component depends only on x1. Hence (curl⁡v,curl⁡v)L2(K)=0.

2.1A1F1F2step 1.1algebra∎

By [F1], every qh∈Wh0=P1(K) has constant gradient, while v is nonconstant on the interior of K. Thus v∉∇Wh0. The kernel of the naive nodal curl-curl energy is therefore strictly larger than the compatible discrete gradient image, and its representative is not unique without a gauge as in [F2].

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

18 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources