Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedjudge 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 two-state uncertain reaction rate has an exact degree-one chaos expansion

Example

Let Y take the values −1 and 1 with probability 1/2 each, choose cutoff p1=1, and let the uncertain positive reaction rate be k(Y)=κ(1+ηY) with κ>0 and ∣η∣<1. Its degree-one chaos expansion is exact, with mean κ and variance κ2η2.

Facts & Assumptions

Given: The symmetric two-point probability law and the rate parameters κ>0, ∣η∣<1.

[F1]

Finite product-law polynomial-chaos input data requires finite moments and a positive-definite monomial Gram matrix through the chosen cutoff.

[F2]

Positive moment Gram matrices produce orthonormal polynomial families constructs the normalized degree-zero and degree-one polynomials.

[F3]

Finite chaos projection, Parseval identity, mean, and variance identifies the constant and first nonconstant coefficients with mean and variance contributions.

Verification

1.1F1F2givenalgebra

The first two moments are E[Y]=0 and E[Y2]=1, so the degree-one Gram matrix is (1001), positive definite. Thus [F1] applies. Gram--Schmidt gives ψ0=1 and ψ1=Y, since Y already has mean zero and unit norm and its leading coefficient is positive.

2.1F3step 1.1algebra

Substitution gives k(Y)=κψ0+κηψ1 at both support points, so the degree-one projection residual is identically zero. Its coefficients are c0=E[k(Y)]=κ and c1=E[k(Y)Y]=κη. By [F3], the mean is c0 and variance is c12=κ2η2.

3.1givenstep 1.1algebra∎

The condition ∣η∣<1 implies 1+ηY≥1−∣η∣>0 at both states, so the reaction rate is positive on its whole support. Every function on this support also has the exact form g(Y)=g(1)+g(−1)2+g(1)−g(−1)2Y; hence this two-dimensional finite basis spans L2(ρ) for this particular law.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

14 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.