Global analysis, analysis on manifolds
The Lean Pool projects whose card's first MSC2020 code is in class 58, “Global analysis, analysis on manifolds”: 1 article.
StructureMonotonicity Formula for Stationary Harmonic Maps#
Formalizes the monotonicity formula for stationary
W^{1,2}_{loc}maps with Euclidean target. The proof is target independent: it uses only the displayed weak gradient and a vanishing domain first variation, so no target-manifold structure enters the argument. From a bundled stationary Sobolev witnessStationaryW12LocMap u Omegait proves, for0 < s <= r < R0, that the normalized energyTheta(Du, a, rho) = rho^(2-n) * integral over B(a, rho) of |Du|^2is monotone:weakTheta Du a s <= weakTheta Du a r, together with the exact increment formulaweakTheta Du a r - weakTheta Du a s = weakMonotonicityRhs Du a s r. The Sobolev layer is project-defined from Mathlib'sMemLp, weak-derivative identities, Bochner integration, and Euclidean measure theory, following the De Giorgi-Nash-Moser engineering choice; the internal proof routes through a radial vector-field first variation, a coarea/Radon-Nikodym radius representation, smooth radial cutoffs, and an absolute-continuity boundary step.
Quoted from the card LeanPool/projects/stationary-harmonic-maps.yaml of LeanPool/lean-pool at commit 2b205bf9, Apache-2.0.
Main results
The card lists 4 main results, each with its informal statement quoted from the card. The scan finds 4 of their declarations in the project's files at commit 2b205bf9. A found declaration links its page in Lean Pool's documentation, which Lean Pool rebuilds daily from its main branch, and its line at this commit.
LeanStationaryHarmonicMaps.StationaryHarmonicMap.StationaryW12LocMap(structure; a main declaration), declared in LeanPool/LeanStationaryHarmonicMaps/StationaryHarmonicMap/StationaryMap.lean, line 37: “The public input package: a domain-variation stationaryW^{1,2}_{loc}Sobolev map bundling a chosen weak gradient (W12LocMapWitness) with the vanishing domain first-variation identity. This is the object users construct to invoke the monotonicity theorem.”LeanStationaryHarmonicMaps.StationaryHarmonicMap.stationaryW12LocMonotonicity_euclidean(theorem), declared in LeanPool/LeanStationaryHarmonicMaps/StationaryHarmonicMap/MainTheorem.lean, line 56: “Witness-style monotonicity: from a stationaryW^{1,2}_{loc}map package, the normalized weak energyweakThetais monotone in the radius,weakTheta Du a s <= weakTheta Du a rfor0 < s <= r < R0.”LeanStationaryHarmonicMaps.StationaryHarmonicMap.stationaryW12LocMonotonicityFormula_euclidean(theorem), declared in LeanPool/LeanStationaryHarmonicMaps/StationaryHarmonicMap/MainTheorem.lean, line 34: “Witness-style monotonicity formula: the increment ofweakThetaequals the nonnegative annular radial-energy term,weakTheta Du a r - weakTheta Du a s = weakMonotonicityRhs Du a s rfor0 < s < r < R0.”LeanStationaryHarmonicMaps.StationaryHarmonicMap.stationarySobolevMonotonicity_euclidean(theorem), declared in LeanPool/LeanStationaryHarmonicMaps/StationaryHarmonicMap/API.lean, line 87: “Componentwise convenience wrapper: the same monotonicity inequality stated directly from the localL^2, measurability, weak-gradient, and first-variation hypotheses, without first constructing the witness package.”
Project
- Authors: Wei Wang.
- Source: Theorems on Regularity and Singularity of Energy Minimizing Maps by Leon Simon, doi:10.1007/978-3-0348-9193-6.
- Project repository: BrookWW/LeanStationaryHarmonicMaps.
- Provenance: mix, which in Lean Pool's terms means people and AI “both contributed substantially” to its Lean proofs.
- License: MIT, under which the project was published; Lean Pool redistributes it under Apache-2.0.
- MSC2020: 58E20, 35J50.
- In Lean Pool: LeanPool/LeanStationaryHarmonicMaps, from the card LeanPool/projects/stationary-harmonic-maps.yaml.
Sources
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