Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedverified 2026-10-22 (gpt-6.1-sol)
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.

Generalized FNO approximation on compact sets

Statement

Assume Countable Choice. Let M=∂Ω×∂Ω for a nonempty bounded smooth domain Ω⊂(0,1)d, d≥2, and let a,b∈N be positive finite channel dimensions. If T:L2(M;Ra)→L2(Ω;Rb) is continuous, then for every compact set C⊂L2(M;Ra) and every ε>0, there exists a generalized Fourier neural operator Ψ:L2(M;Ra)→L2(Ω;Rb) using a fixed smooth globally Lipschitz nonpolynomial activation and the boundary charts of the statement such that sup⁡h∈C∥T(h)−Ψ(h)∥L2(Ω;Rb)<ε.

Facts & Assumptions

Given: Ω, M, T, compact C, and ε as in the statement.

[F1]

There are bounded chart maps Ga:L2(M;Ra)→⨁j,kL2(T2d−2;Ra) and La in the reverse direction, obtained by applying the scalar chart maps componentwise, with LaGa=I. (Boundary chart lifting and compact-uniform Fourier projections)

[F2]

On a torus, a Fourier neural operator with the specified activation uniformly approximates every continuous map on every compact input set in the L2 norm. (Fourier neural operators approximate continuous maps uniformly on compact torus sets)

[F3]

Zero extension from L2(Ω) to L2(Td) is isometric because Ω⊂(0,1)d; the product extension constant in the last d−2 coordinates is also isometric under normalized Haar measure.

[F4]

The generalized-FNO architecture contains the zero operator by setting its finite-layer coefficients to zero. (Generalized Fourier neural operator on a compact manifold)

[A1]

Countable Choice is assumed at the finite boundary-chart partition interface. (The Axiom of Countable Choice (ACω))

Proof

Given: Countable Choice and the data in the statement.

1.1A1F1F3F4givenconstruct

If C=∅, choose the zero generalized FNO from [F4]; the uniform error condition is vacuous. Otherwise let n=2d−2 and let J0:L2(Ω;Rb)→L2(Td;Rb) be zero extension. Let Id,n:L2(Td;Rb)→L2(Tn;Rb) send g(x1,…,xd) to the function constant in the last n−d=d−2 coordinates. The chart-pair components identify ⨁j,kL2(Tn;Ra) with L2(Tn;RJ2a). Define S:=Id,nJ0TLa on this direct-sum space. This is continuous by [F1] and [F3], and Ga(C) is compact because Ga is continuous and C is compact.

2.1F2step 1.1choose

Apply [F2] to the continuous map S:L2(Tn;RJ2a)→L2(Tn;Rb) on compact Ga(C) with tolerance ε. There is a torus FNO Φ such that sup⁡u∈Ga(C)∥S(u)−Φ(u)∥L2(Tn;Rb)<ε.

3.1F1F3step 1.1step 2.1givenalgebra∎

Set T0={()} with unit mass, so the following integral is the identity when d=2. For any finite channel dimension c, define Ac:L2(Tn;Rc)→L2(Td;Rc) by (Acv)(x)=∫Td−2v(x,z) dz, componentwise. Cauchy–Schwarz on the unit-measure torus and Fubini give ∥Ac∥≤1. Let R be restriction from Td to Ω, also of norm at most one. Since AbId,n=I, RJ0=I, and LaGa=I, the generalized FNO Ψ:=R∘Ab∘Φ∘Ga satisfies ∥T(h)−Ψ(h)∥L2(Ω;Rb)=∥RAb(S(Gah)−Φ(Gah))∥L2(Ω;Rb)≤∥S(Gah)−Φ(Gah)∥L2(Tn;Rb)<ε for every h∈C. Write the torus FNO as Φ=QΦ∘Φ0, where Φ0 ends before its final constant affine channel map QΦ(v)=Wv+c and has r output channels. Its initial affine channel lift is composed into the first affine part of Φ0 after Ga. Normalized averaging commutes with the final map, AbQΦ=QΦAr, and restriction commutes with the same pointwise map. Hence RAbΦGa=QΦRArΦ0Ga. This has the generalized architecture: identity pointwise lift, chart lift Ga, torus layers Φ0, the constant-kernel dimension reduction Ar, restriction R, and final pointwise affine map QΦ.

Depends on

Used by

Dependency tree · two levels

16 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