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.
Affine diffusion forms are well-defined on Bochner L2
Statement
For the model of Uniformly elliptic affine random diffusion model, let . For , the scalar functions and have measurable integrable versions in the convention of Bochner L2 space for a finite product-law input, so and are well-defined on equivalence classes. They satisfy and
Facts & Assumptions
Given: A bounded-support product law, the affine coefficient and forcing, and the uniform coefficient bounds in Uniformly elliptic affine random diffusion model.
For every , the pointwise form satisfies and ; is bounded and affine (Uniformly elliptic affine random diffusion model).
Strongly measurable maps are almost-everywhere limits of measurable finite-valued simple maps (Strongly measurable Banach-valued function).
The scalar integral Cauchy--Schwarz inequality is Cauchy-Schwarz inequality for .
Bochner L2 space for a finite product-law input identifies with strongly measurable square-integrable maps modulo almost-everywhere equality.
Proof
Choose finite-valued measurable simple maps converging respectively to almost everywhere, using [F2]. On every intersection of their finite partition sets, is an affine continuous scalar function of by the displayed coefficient formula in [F1]. It is therefore measurable. On the common full-measure set where both approximations converge, continuity of the bounded bilinear form gives convergence to . Setting the scalar function to zero on the common measurable exceptional null set gives a measurable version; an arbitrary modification on that set need not be measurable. The integral uses this version, as in [F4].
For a finite-valued simple converging almost everywhere to , the scalar function is measurable on each partition piece because is affine continuous into the finite-dimensional span of . Its pointwise limit on the full-measure convergence set is , so setting the limit to zero on the measurable exceptional null set gives a measurable version in the convention [F4]. Since , ; hence .
The pointwise bound [F1] gives . Both norm functions are square-integrable, so [F3] makes this majorant integrable. The form integral is finite and depends only on the almost-everywhere classes by [F4].
Pointwise duality gives . Apply [F3] to obtain the stated integrability and functional bound.
Integrating the pointwise coercivity bound [F1] gives . Integrating the pointwise continuity bound and applying [F3] as in step 2.1 gives . These estimates prove the remaining assertions.
Depends on
Used by
- Affine random diffusion has a measurable Bochner solution Theorem
- Stochastic Galerkin coercivity and Cea quasi-optimality Theorem
Cited to discharge well-definedness by Uniformly elliptic affine random diffusion model.
Dependency tree · two levels
17 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
- Bruno Sudret, Polynomial Chaos Expansions and Stochastic Finite Element Methods, §4 (standard reference, not scraped)
- Ivo Babuška, Fabio Nobile, and Raúl Tempone, A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data, §1 (standard reference, not scraped)