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✓ 9 results · all verified · 8 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 1 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Semi-Lagrangian characteristic transport

1 · Prerequisites

2 · Summary

This page develops periodic semi-Lagrangian transport for the scalar equation ut+b⋅∇u=0. Its concrete scheme uses explicit Euler backtracking and tensor-product multilinear interpolation. Convex interpolation weights give sup-norm stability for arbitrary foot displacement, while the proved local foot and interpolation defects yield a separate finite-time error bound. The bound requires both time and spatial refinement; the lack of an advective CFL condition does not remove the sufficient consistency condition h2/Δt→0.

The conservative density branch uses characteristic departure cells, exact overlap volumes, and the flow volume factor. This Lebesgue-volume branch assumes Countable Choice and uses finite measurable source and target partitions with positive cell volumes. Pointwise interpolation and overlap remapping are separate schemes, and the former is not asserted to conserve mass. The event item records the local error for a transverse first-hit boundary bracket only; this page makes no full global error claim for a nonperiodic boundary branch. Higher-order interpolation needs its own actual operator bound and defect estimate.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Periodic tensor grids and multilinear interpolation

Definition

Let d≥1, let ℓ1,…,ℓd>0, and let N1,…,Nd≥1 be integers. The periodic rectangular box is Tℓd:=Rd/(ℓ1Z×⋯×ℓdZ). Set hj:=ℓj/Nj. Its uniform tensor grid has nodes xi=(i1h1,…,idhd), indexed by 0≤ij<Nj, with indices understood modulo Nj.

For node data Vi and a lift x∈Rd of a point on the torus, define qj:=⌊xj/hj⌋,θj:=xj/hj−qj∈[0,1), and for ϵ∈{0,1}d define wϵ(x):=∏j=1dθjϵj(1−θj)1−ϵj. The tensor-product multilinear interpolant is IhV(x):=∑ϵ∈{0,1}dwϵ(x)V([q1+ϵ1]N1,…,[qd+ϵd]Nd), where [k]N is the residue in {0,…,N−1}. The formula is unchanged when a period ℓj is added to xj, so it defines an interpolant on Tℓd. If Nj=1, the two corner indices in that coordinate coincide; the formula still gives the same node value.

For a continuous periodic function v, Ihv means the interpolant of its grid samples. Put h:=max⁡jhj.

Convention diagram

The picture shows one lifted coordinate cell. The weights are 1−θ at the left endpoint and θ at the right endpoint; periodic wrapping only changes the node indices.

xjxj+1exµhj(1¡µ)hj

The diagram fixes the coordinate and weight convention only. The multidimensional interpolant is the product of the coordinate weights in the displayed formula.

TheoremStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A periodic C1 velocity has a global characteristic flow on a finite horizon

Statement

Let d≥1, T>0, ℓj>0, and let J⊆R be an open interval containing [0,T]. Suppose b:J×Rd→Rd is C1 and periodic in xj with period ℓj for every j. Choose a compact interval K⊂J with [0,T]⊂int⁡K, and put MK:=sup⁡K×[0,ℓ1]×⋯×[0,ℓd]∥b∥2. For every s∈[0,T] and x∈Rd, there is a unique solution X(⋅;s,x) of X′(t)=b(t,X(t)),X(s;s,x)=x, on the whole interval [0,T]. The lifted flow is periodic-equivariant: X(t;s,x+kℓ)=X(t;s,x)+kℓ(k∈Zd), where kℓ=(k1ℓ1,…,kdℓd). If MT:=sup⁡[0,T]×[0,ℓ1]×⋯×[0,ℓd]∥b∥2, then ∥X(t;s,x)−x∥2≤MT∣t−s∣(t∈[0,T]). Thus each characteristic stays in a compact tube [0,T]×B‾(x,MTT+1).

Facts & Assumptions

Given: d,T,ℓ,J,K,b,s,x as in the Statement.

[F1]

Linear transport equations and their characteristic flow: a characteristic solves X′(t)=b(t,X(t)); a single-valued flow requires existence and uniqueness for each initial value.

[F2]

Picard-Lindelöf local existence and uniqueness for first-order systems: a continuous vector field locally Lipschitz in the state has a unique local solution; on a compact cylinder with field bound M0 and state Lipschitz constant L0, it applies on a symmetric interval of radius δ when δM0≤r and L0δ<1 for the cylinder state radius r.

[F3]

A first-order initial value problem is equivalent to its Volterra integral equation: a solution satisfies X(t)=X(s)+∫stb(r,X(r)) dr with the oriented integral.

[F4]

For a≤b and f:[a,b]→Rm integrable when a<b, ∥∫abf∥2≤∫ab∥f∥2; for a<b, ∥f∥2 is integrable: ∥∫abf∥2≤∫ab∥f∥2 for integrable vector-valued f and ordered endpoints a≤b; a reversed oriented integral is first negated and rewritten with ordered endpoints.

[F5]

On a convex open set, a uniform bound ∥Df(z)v∥2≤M∥v∥2 implies ∥f(y)−f(x)∥2≤M∥y−x∥2: on a convex open set, a uniform derivative bound ∥Df(z)v∥2≤L∥v∥2 gives the corresponding L-Lipschitz bound.

Proof

1.1givenF2F5choose

Periodicity and continuity on the compact cylinder K×[0,ℓ1]×⋯×[0,ℓd] give finite bounds MK:=sup⁡∥b∥2 and L:=sup⁡∥Dxb∥ there. The same bounds hold on all spatial translates. By [F5] each time slice is globally L-Lipschitz in x. Since [0,T]⊂int⁡K, choose δ>0 small enough that every radius-δ time interval centered in [0,T] lies in K, δ(MK+1)≤1, and δ(L+1)<1. On each corresponding unit-radius state cylinder, [F2] therefore gives a unique local solution through its center.

2.1givenF2step 1.1construct

Partition the finite interval between s and any t∈[0,T] into finitely many subintervals of length at most δ. Apply [F2] successively at each endpoint, using the preceding endpoint value as the next initial state. The resulting curves agree on their shared endpoints and have the same derivative b(r,X(r)) there, so they concatenate to a solution on [0,T]. Repeated local uniqueness from [F2] proves uniqueness on the concatenated interval. This is a finite construction and selects no family of local solutions over an infinite index set.

3.1givenF1F3F4step 2.1algebra∎

For the constructed solution on [0,T], [F3] and [F4] give ∥X(t;s,x)−x∥2≤∫min⁡(s,t)max⁡(s,t)∥b(r,X(r))∥2 dr≤MT∣t−s∣. Thus its graph lies in the stated compact tube. For a period vector kℓ, the curve X(t;s,x+kℓ)−kℓ solves the same IVP as X(t;s,x) by periodicity of b; uniqueness gives periodic equivariance.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A transverse backward boundary hit has a certified time and trace error bound

Statement

Let J=[a,b] be a compact bracket, let y be an exact incoming characteristic on J, and let g(t,x) be a boundary guard that is Lg-Lipschitz in its state argument on a tube containing the exact path and a dense reconstruction U. Suppose h(t):=g(t,y(t)) has a root τ∈J and a declared orientation σ∈{−1,1} with σh′(t)≥ν>0 throughout J. If sup⁡t∈J∥y(t)−U(t)∥2≤η,∣g(τ^,U(τ^))∣≤ε,τ^∈J, then the matched first-hit time satisfies ∣τ^−τ∣≤Δ:=Lgη+εν. If the boundary value β(t,x) is Lipschitz in time and state with constants Ltβ,Lxβ on the same tube and ∥y′(t)∥2≤M, then ∣β(τ^,U(τ^))−β(τ,y(τ))∣≤LtβΔ+Lxβ(η+MΔ). This is a local one-hit bound. It gives no conclusion at grazing, unresolved, or simultaneous events.

Facts & Assumptions

Given: A smooth mode characteristic, a boundary guard and orientation, a matched transverse first-hit bracket, a dense-output error bound, a reported guard residual, and the stated boundary-data Lipschitz bounds.

[F1]

Smooth hybrid ODE modes, guards, resets, and deterministic event priority: a smooth mode follows its ODE until a selected guard event; the event orientation is part of the guard data.

[F2]

The derivative of a guard along an ODE mode is its Lie derivative: along y′=b(t,y), the guard derivative is h′(t)=∂tg(t,y(t))+Dxg(t,y(t))b(t,y(t)).

[F3]

A transverse hybrid event time is stable under dense-output and root error: dense state error η, guard residual ε, guard state-Lipschitz constant Lg, and transverse margin ν imply ∣τ^−τ∣≤(Lgη+ε)/ν on the matched bracket.

[F4]

A first-order initial value problem is equivalent to its Volterra integral equation: a mode trajectory increment equals the oriented integral of its vector field.

[F5]

For a≤b and f:[a,b]→Rm integrable when a<b, ∥∫abf∥2≤∫ab∥f∥2; for a<b, ∥f∥2 is integrable: for ordered endpoints c≤d, ∥∫cdf∥2≤∫cd∥f∥2; reversed oriented integrals must first be rewritten with ordered endpoints.

Proof

1.1givenalgebra

If a=b, then τ^=τ=a, so the time error is zero and hence at most Δ. The stated bounds give ∥U(a)−y(a)∥2≤η; the state-Lipschitz bound for β gives ∣β(a,U(a))−β(a,y(a))∣≤Lxβη≤LtβΔ+Lxβ(η+MΔ). Here Δ≥0 from the error and residual bounds, ν>0, and the nonnegative Lipschitz constant Lg, while M,Ltβ,Lxβ≥0. Assume a<b below.

1.2givenF1F2F3

By [F2], the transversality hypothesis is the mode derivative condition required by [F3]. Apply [F3] to this matched bracket with the stated η,ε,Lg,ν; it yields the unique transverse root and the displayed bound ∣τ^−τ∣≤Δ.

2.1givenF4F5step 1.2algebra∎

By [F4], the increment is the oriented integral from τ to τ^. Set c=min⁡(τ,τ^) and d=max⁡(τ,τ^); changing orientation only negates the vector integral, so [F5] gives ∥y(τ^)−y(τ)∥2≤∫cd∥y′(r)∥2 dr≤M(d−c)=M∣τ^−τ∣≤MΔ. The triangle inequality with the dense-output bound gives ∥U(τ^)−y(τ)∥2≤η+MΔ. Apply the time and state Lipschitz inequalities for β to obtain the second displayed bound.

Remarks

An inflow/exit implementation must use a certified first-hit bracket and return unresolved when the bracket or orientation is not certified. This local result does not propagate errors through resets or an event list; those require the full finite-separated-event hypotheses of Finite separated transverse events give an event-aligned hybrid trajectory error bound.

AlgorithmAlgorithm: AI-adaptedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Explicit Euler characteristic backtracking with multilinear interpolation

Definition

Inputs are a periodic grid from Periodic tensor grids and multilinear interpolation, a finite horizon T>0, a positive integer M of time steps, a periodic C1 velocity b, periodic initial data u0, node values Ui0=u0(xi), and a nonnegative integer step budget B. Write Δt=T/M and tn=nΔt. The output is either success with U0,…,UM, or budget-exhausted with no partial final state.

  1. If B<M, return budget-exhausted before changing the input data.
  2. For n=0,…,M−1 and each arrival node xi, compute the explicit backward Euler foot lift x~i n:=xi−Δt b(tn+1,xi). Reduce each coordinate modulo its period when evaluating the periodic interpolant.
  3. Set Uin+1:=IhUn(x~i n).
  4. After the M finite updates, return success with all node arrays.

The finite step budget counts time updates. Every update evaluates one foot per grid node and a finite sum of 2d interpolation terms. The procedure imposes no bound on ∥b∥Δt/h; the floor-and-fraction convention in the interpolation definition applies even when a foot crosses many grid cells.

Remarks

This algorithm is the periodic-box branch. It does not wrap a foot through a nonperiodic boundary. An inflow/exit implementation must instead use an explicit first-hit oracle and return an unresolved status when the first hit or its error certificate is not resolved; A transverse backward boundary hit has a certified time and trace error bound records the transverse error interface. No grazing, simultaneous, or unbounded event sequence is hidden in the periodic update.

The algorithm defines tensor-product multilinear interpolation only. A higher-order replacement needs its own operator-norm or limiter bound and its own interpolation defect estimate; high order alone does not imply L∞ contractivity.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Periodic multilinear interpolation is convex and L-infinity contractive

Statement

For the periodic tensor interpolant of Periodic tensor grids and multilinear interpolation, every value is a convex combination of the 2d corner data. Consequently nonnegative data remain nonnegative, the interpolant lies between the minimum and maximum corner values, and for any node arrays V,W, ∥IhV−IhW∥L∞(Tℓd)≤∥V−W∥ℓ∞. These conclusions hold at every foot, with no displacement-to-cell-size restriction.

Facts & Assumptions

Given: The periodic grid, a lifted query point x, and finite real node arrays V,W.

[F1]

Periodic tensor grids and multilinear interpolation: the coordinate fractions satisfy 0≤θj<1 and the corner weights are wϵ=∏jθjϵj(1−θj)1−ϵj, with wrapped node indices.

Proof

1.1givenF1algebra

Each factor θj and 1−θj is nonnegative, so every wϵ(x)≥0. Summing over all binary vectors factorizes as ∑ϵwϵ(x)=∏j=1d((1−θj)+θj)=1. Thus IhV(x) is a convex combination of its corner data, even when wrapping makes two corner indices coincide.

2.1step 1.1givenalgebra

If Vi≥0 at every node, each summand in the interpolant is nonnegative, so its value is nonnegative; the same convex-combination identity bounds it by the minimum and maximum corner data.

3.1givenF1step 1.1algebra∎

At each x, [F1] and the triangle inequality give ∣IhV(x)−IhW(x)∣≤∑ϵwϵ(x)∣Vi(ϵ)−Wi(ϵ)∣≤∥V−W∥ℓ∞∑ϵwϵ(x)=∥V−W∥ℓ∞. Taking the supremum over x proves the stated contraction; no estimate in this argument uses the displacement size.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A periodic C1 characteristic map is an orientation-preserving diffeomorphism

Statement

Under the hypotheses of A periodic C1 velocity has a global characteristic flow on a finite horizon, fix s,t∈[0,T] and let Φt,s(x):=X(t;s,x) on the periodic lift Rd. Then Φt,s is a C1 diffeomorphism with inverse Φs,t, and det⁡DxΦt,s(x)>0(x∈Rd).

Facts & Assumptions

Given: The periodic C1 flow from time s to time t on Rd.

[F1]

A periodic C1 velocity has a global characteristic flow on a finite horizon: the lifted characteristic exists uniquely over [0,T] for every initial point.

[F2]

Transport characteristics depend C^1 on the initial position: for a C1 field, the flow depends C1 on its initial position over a common compact time interval.

[F3]

The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a): derivatives of a composition multiply in the order D(g∘f)=Dg(f)Df.

[F4]

A finite square real matrix is invertible if and only if its determinant is nonzero: a finite square real matrix is invertible exactly when its determinant is nonzero.

[F5]

For every fixed finite size at least one, the determinant of a real square matrix is a polynomial in its matrix entries: for fixed finite dimension, determinant is a polynomial and hence a continuous function of the matrix entries.

[F6]

Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on [a,b] takes every value between f(a) and f(b): a continuous real function on an interval cannot pass from a positive value to a negative value without taking zero.

Proof

1.1givenF1F2algebra

By [F1], both the forward flow Φt,s and the reverse-time flow Φs,t are defined. Uniqueness of the ODE implies that following the same characteristic first from s to t and then backward to s returns the initial point; likewise in the opposite order. Thus Φs,t∘Φt,s=id and Φt,s∘Φs,t=id. By [F2] both maps are C1, so Φt,s is a C1 diffeomorphism.

2.1step 1.1F3F4algebra

Applying [F3] to Φs,t∘Φt,s=id gives DΦs,t(Φt,s(x))DΦt,s(x)=Id. Hence DΦt,s(x) is invertible, and [F4] gives det⁡DΦt,s(x)≠0.

3.1givenF2F5F6step 2.1algebra∎

For fixed s,x, the function r↦det⁡DΦr,s(x) is continuous by [F2] and [F5], and equals 1 at r=s. It never vanishes by step 2.1. If it were negative at some r, [F6] on the interval between s and r would force a zero, a contradiction. Therefore it is positive at every r, including the chosen t.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Tensor multilinear interpolation has a second-derivative error bound

Statement

Let u∈C2(Tℓd) and let hj be the coordinate mesh widths in Periodic tensor grids and multilinear interpolation. Then ∥Ihu−u∥L∞(Tℓd)≤18∑j=1dhj2∥∂jju∥L∞(Tℓd). The estimate includes grid nodes and periodic cell endpoints.

Facts & Assumptions

Given: A periodic C2 function u, positive coordinate widths hj, and the tensor-product interpolant.

[F1]

Periodic tensor grids and multilinear interpolation: the tensor interpolant is the product of the one-dimensional linear endpoint weights in each coordinate.

[F2]

Pointwise Lagrange interpolation remainder: for two distinct interpolation nodes a<b, the degree-one interpolant p to a C2 function satisfies u(x)−p(x)=0 at a node and otherwise u(x)−p(x)=12u′′(ξ)(x−a)(x−b) for some ξ between the nodes and query point.

[F3]

Periodic multilinear interpolation is convex and L-infinity contractive: each one-coordinate linear interpolation operator is nonexpansive in the sup norm because its endpoint weights are nonnegative and sum to one.

Proof

1.1givenF2algebra

Fix one coordinate interval [a,b] of length hj and hold the other coordinates fixed. If xj is an endpoint, the linear interpolant is exact by [F2]. Otherwise [F2] gives ∣u(x)−Iju(x)∣≤12∥∂jju∥∞(xj−a)(b−xj). Writing θ=(xj−a)/hj∈(0,1), the product equals hj2θ(1−θ)≤hj2/4, hence the one-coordinate error is at most hj2∥∂jju∥∞/8.

1.2F1algebra

Write the tensor interpolant as I1⋯Id, where Ij interpolates only coordinate j. The telescoping identity u−I1⋯Idu=(u−I1u)+I1(u−I2u)+⋯+I1⋯Id−1(u−Idu) follows by cancellation of adjacent terms.

2.1givenF1F3step 1.1algebra∎

Apply the one-coordinate estimate of step 1.1 to each term before its preceding-coordinate operators. Every preceding product I1⋯Ij−1 is sup-norm nonexpansive by [F3], so the norm of the jth term is at most hj2∥∂jju∥∞/8. Summing the d bounds proves the result. Periodic endpoint cells use the periodic lift of u, which has the same derivative suprema, and at every grid node the interpolation is exact by the definition.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Explicit Euler gives a second-order local backward-foot defect

Statement

Let b∈C1([0,T]×Rd;Rd) be periodic in space, and set M:=sup⁡∥b∥2,Lx:=sup⁡∥Dxb∥,Lt:=sup⁡∥∂tb∥2, where the suprema are over one full periodic time-space cylinder. For 0≤tn<tn+1≤T, Δt=tn+1−tn, and an arrival lift x, let X(tn;tn+1,x) be the exact backward characteristic foot. The explicit Euler foot x−Δt b(tn+1,x) satisfies ∥X(tn;tn+1,x)−(x−Δt b(tn+1,x))∥2≤12(LxM+Lt)(Δt)2. The estimate is local for every positive Δt; it requires no relation between displacement and grid width.

Facts & Assumptions

Given: The periodic C1 field, its finite derivative bounds, a time interval [tn,tn+1], and an arrival point x.

[F1]

Explicit Euler characteristic backtracking with multilinear interpolation: the algorithm uses the lifted explicit backward foot x−Δt b(tn+1,x).

[F2]

A periodic C1 velocity has a global characteristic flow on a finite horizon: the exact characteristic exists on the whole interval, and ∥b∥2≤M on its periodic tube.

[F3]

A first-order initial value problem is equivalent to its Volterra integral equation: the exact characteristic increment is the oriented integral of b(s,X(s)).

Proof

1.1givenF2F3F4

By [F2] and [F3], for s∈[tn,tn+1] the backward characteristic satisfies X(s)=x−∫stn+1b(r,X(r)) dr. Applying [F4] and the velocity bound gives ∥X(s)−x∥2≤M(tn+1−s).

2.1givenF1F3F4step 1.1algebra∎

Subtracting the explicit Euler foot from the exact foot and using [F3] writes the difference as the integral from t_n to t_{n+1} of b(t_{n+1},x)-b(s,X(s)). The derivative bounds make its integrand norm at most L_x times the state difference plus L_t times (t_{n+1}-s); by step 1.1 the state difference is at most M(t_{n+1}-s). Integrating the resulting bound (L_x M+L_t)(t_{n+1}-s) gives one half (L_x M+L_t)(Delta t)^2.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passOpen item page →

Characteristic departure cells are measurable and have positive finite volume

Statement

Assume the Axiom of Countable Choice. Let Ω=∏j=1d(0,ℓj) be the bounded open fundamental box used for Lebesgue measure, and let Q=∏j=1d[0,ℓj) be its half-open grid-indexing copy. Let Φ be the periodic C1 characteristic diffeomorphism of A periodic C1 characteristic map is an orientation-preserving diffeomorphism, and let Ai⊆Q be a Lebesgue-measurable arrival cell with ∣Ai∣:=λd(Ai∩Ω)>0. Then Di:=Φ−1(Ai), interpreted on the torus and represented in Ω, is Lebesgue measurable and satisfies 0<∣Di∣<∞. This includes every finite tensor-grid cell with nonempty interior. The departure cells form a partition up to null sets whenever a finite family of arrival cells does; an exact torus partition pulls back to an exact torus partition.

Facts & Assumptions

Given: Countable Choice, a periodic C1 characteristic diffeomorphism, the fundamental boxes Ω,Q, and measurable positive-volume arrival cells.

[A1]

The Axiom of Countable Choice (ACω) is assumed for the Lebesgue-measure suppliers below; no additional choice is used.

[F1]

A periodic C1 characteristic map is an orientation-preserving diffeomorphism gives the lifted flow and its inverse as C1 diffeomorphisms; A periodic C1 velocity has a global characteristic flow on a finite horizon gives their equivariance under the period lattice.

[F2]

A C^1 diffeomorphism maps Lebesgue measurable sets to Lebesgue measurable sets: under Countable Choice a C1 diffeomorphism between open Euclidean sets maps measurable sets to measurable sets. Apply this also to translations and to Φ−1.

[F3]

A C^1 diffeomorphism maps Lebesgue null sets to Lebesgue null sets: under Countable Choice the same maps preserve null sets.

[F5]

A box in Rn with parameters ai≤bi is Lebesgue measurable of measure ∏i<n(bi−ai), whichever of its faces are included: ∣Ω∣=∏jℓj<∞; bounded boxes with a degenerate side are null. Thus the union H:=⋃j=1d⋃k∈Z{x:xj=kℓj} of tile faces is null, by covering each face with countably many bounded degenerate boxes and applying [F4].

[F6]

Measures are monotone: measurable inclusions preserve measure order.

Proof

1.1givenA1F1F2F4construct

Put Λ=∏jℓjZ and A^i=⋃k∈Λ(Ai+k). By [F2] and [F4] this countable periodic lift is measurable, and Ei:=Φ−1(A^i) is measurable by [F1] and [F2]. Hence Di=Ei∩Ω is measurable. Equivariance makes Ei periodic and makes the torus preimage independent of representatives.

2.1givenA1F1F3F4F5F6step 1.1algebra

If ∣Di∣=0, [F3] and [F4] make ⋃k∈Λ(Di+k) null. Every point of Ei outside H lies in one open tile, so periodicity gives Ei⊆H∪⋃k∈Λ(Di+k). By [F4] and [F5], Ei is null; [F3] then makes Φ(Ei)=A^i null. This contradicts Ai∩Ω⊆A^i and ∣Ai∣>0 by [F6]. Thus ∣Di∣>0. Also Di⊆Ω, so [F5] and [F6] give ∣Di∣≤∏jℓj<∞.

3.1givenA1F1F3F4F5F6step 1.1step 2.1algebra∎

Bijectivity on the torus preserves exact unions and disjointness. For a partition only up to null sets, lift its omitted set and pairwise overlaps periodically; these are null by [F3]–[F5]. Their inverse images are null by [F1] and [F3], so restricting to Ω gives a departure partition up to null sets. Finally, a half-open tensor cell with nonempty interior is measurable with positive volume by [F5], so it is included in the result.

DefinitionDefinition: AI-adaptedProof: Not applicableOpen item page →

Characteristic-cell overlap remapping for a conservative density

Definition

Assume the Axiom of Countable Choice (The Axiom of Countable Choice (ACω)) for this Lebesgue-measurable-volume branch. Work on the periodic box Tℓd of Periodic tensor grids and multilinear interpolation. For measure calculations use the bounded open fundamental domain Ω:=∏j=1d(0,ℓj); the period faces omitted from the half-open grid-indexing copy Q=∏j=1d[0,ℓj) are Lebesgue-null by A box in Rn with parameters ai≤bi is Lebesgue measurable of measure ∏i<n(bi−ai), whichever of its faces are included. For a periodic measurable set E, define ∣E∣:=λd(E∩Ω), where λd is the d-dimensional Lebesgue measure of Lebesgue measurable sets, the family L(Rn), and the restricted set function λn. Let {Cj}j=1J be a finite measurable source-cell partition and {Ai}i=1J a finite measurable arrival-cell partition of Ω up to null sets, with every cell of positive volume; their grid labels use the half-open convention in Q. Let Φ=Φtn+1,tn be the exact characteristic map from time tn to tn+1. Its inverse images Di:=Φ−1(Ai) are the departure cells; Characteristic departure cells are measurable and have positive finite volume gives their measurability and positive finite volume, while A periodic C1 characteristic map is an orientation-preserving diffeomorphism gives the diffeomorphism and positive-Jacobian convention.

Given a source cell-average density ρjn on Cj, set ρ‾i:=1∣Di∣∑j=1J∣Di∩Cj∣ρjn,ρin+1:=∣Di∣∣Ai∣ρ‾i. The first formula is the exact intersection-volume remap of Intersection-volume conservative remapping from the source partition to the departure partition. The second is the characteristic volume factor that converts the average on a departure cell into the transported mass per arrival-cell volume. All intersections and volumes are exact in this mathematical definition; a geometric approximation or quadrature rule would require a separate error term.

Remarks

The batch-63 PE17 definition is a draft carrier and its current body has been inspected. It defines the exact overlap weights ∣Tj∩Si∣ and target-cell average for finite measurable partitions of a bounded finite-positive-measure domain; here take the open box Ω, source cells Sj=Cj∩Ω, and target cells Ti=Di∩Ω. Countable Choice is the axiom used locally for Borel-to-Lebesgue measurability and the finite-volume branch. The separate batch-63 theorem that proves conservation and positivity also assumes Countable Choice, as recorded in the dependent theorem.

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Periodic semi-Lagrangian error is controlled by local foot and interpolation defects

Statement

Fix T>0 and a periodic box with grid width h=max⁡jhj. Let u∈C1([0,T];C2(Tℓd)) solve the nonconservative linear transport equation ut+b⋅∇u=0, with the periodic C1 velocity and global flow of A periodic C1 velocity has a global characteristic flow on a finite horizon. Consider N equal time steps Δt=T/N and node updates Uin+1=(JhUn)(x~i n), where Jh reconstructs a nodal array as a periodic function and x~i n is a computed backward foot. Suppose constants Λ≥1, CI≥0, and CF≥0 are uniform over the refinement family and independent of h,Δt,N. Assume ∥JhV−JhW∥L∞≤Λ∥V−W∥ℓ∞ for all node arrays, the pointwise interpolation defect at each computed foot is at most CIhr+1 for the exact samples uin=u(xi,tn), and, for compatible lifts of the computed and exact feet, the foot error satisfies ∥x~i n−X(tn;tn+1,xi)∥2≤CF(Δt)q+1, for integers q,r≥1. If Lu:=sup⁡[0,T]×Tℓd∥∇u∥2 and En:=max⁡i∣Uin−u(xi,tn)∣, then, with δh:=CIhr+1+ΛLuCF(Δt)q+1, EN≤ΛNE0+δh∑k=0N−1Λk. In the contractive case Λ=1, this becomes EN≤E0+T(CIhr+1Δt+LuCF(Δt)q). For the explicit Euler and tensor multilinear algorithm, Explicit Euler gives a second-order local backward-foot defect, Tensor multilinear interpolation has a second-derivative error bound, and Periodic multilinear interpolation is convex and L-infinity contractive give q=r=1 and Λ=1. Thus convergence for this branch follows, for example, when E0→0, Δt→0, and h2/Δt→0. No classical advective CFL restriction is used; the joint consistency refinement is still required by this bound.

Facts & Assumptions

Given: A smooth periodic transport solution, a finite time grid, a bounded-stability interpolation operator, a pointwise interpolation defect bound, a certified local backward-foot error, and initial grid error E0.

[F1]

Linear transport equations and their characteristic flow: for ut+b⋅∇u=0, the spatial characteristic solves X′=b(t,X).

[F2]

Explicit Euler characteristic backtracking with multilinear interpolation: the default update is obtained by evaluating the previous grid field at an approximate backward foot and applying periodic multilinear interpolation.

[F3]

A periodic C1 velocity has a global characteristic flow on a finite horizon: the exact backward characteristic exists on the full time interval.

[F4]

The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a): differentiating s↦u(X(s),s) gives ut(X(s),s)+∇u(X(s),s)⋅X′(s).

[F5]

Explicit Euler gives a second-order local backward-foot defect: for the default explicit Euler foot, the local error is at most 12(LxM+Lt)(Δt)2.

[F6]

Periodic multilinear interpolation is convex and L-infinity contractive: for the default multilinear interpolant, Λ=1.

[F7]

Tensor multilinear interpolation has a second-derivative error bound: for the default interpolant the pointwise defect is at most 18∑jhj2∥∂jju∥∞, so one may take r=1 and CI=18∑j∥∂jju∥∞ when h=max⁡jhj.

Proof

1.1givenF1F3F4algebra

Along the exact characteristic X(⋅;tn+1,xi), [F1] gives X′=b(t,X). The chain rule [F4] therefore gives ddtu(X(t),t)=ut+b⋅∇u=0. Hence u(xi,tn+1)=u(X(tn;tn+1,xi),tn).

2.1givenF2F5F6F7step 1.1algebra

Add and subtract (Jhun)(x~i n) and u(x~i n,tn). The operator bound gives a first difference at most ΛEn; the interpolation defect contributes at most CIhr+1; and the spatial Lipschitz bound for u plus the foot error contributes at most LuCF(Δt)q+1. Since Λ≥1, this is at most δh+ΛEn. Taking the maximum over nodes yields En+1≤ΛEn+δh.

3.1givenstep 2.1inductionalgebra

Induction on n in the recurrence gives En≤ΛnE0+δh∑k=0n−1Λk: at n=0 the sum is empty, and the recurrence multiplies the preceding finite sum by Λ and adds its next term. Taking n=N proves the first bound.

4.1givenF5F6F7step 3.1algebra∎

If Λ=1, the sum in step 3.1 is N=T/Δt, so substituting the definition of δh gives the second bound. The default Euler and tensor multilinear branch has the stated local constants by [F5]–[F7]. Therefore the displayed sufficient refinement conditions make both error terms and the initial error tend to zero. No step uses ∥b∥Δt/h≤1; consistency follows from the two explicit defect terms rather than a classical advective CFL condition.

Remarks

For Λ>1, the geometric sum in the first estimate is not uniformly bounded as N grows. A high-order interpolation or limiter must therefore supply its actual operator bound and local defect before this theorem yields a stable convergence claim. This theorem is for the periodic branch; a nonperiodic boundary branch must separately account for first-hit and boundary-data error as in A transverse backward boundary hit has a certified time and trace error bound.

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Characteristic-cell overlap remapping preserves mass and positivity

Statement

Assume Countable Choice and the finite measurable positive-volume partition hypotheses in Characteristic-cell overlap remapping for a conservative density. For nonnegative source averages ρjn≥0, the characteristic-cell overlap remap satisfies ρin+1≥0 for every arrival cell and ∑i∣Di∣ρ‾i=∑j∣Cj∣ρjn. In particular, it satisfies ∑i∣Ai∣ρin+1=∑j∣Cj∣ρjn. The proof applies Overlap remapping is an L2 projection, conservative and positive to the source partition {Cj} and departure partition {Di}; its stated Countable Choice assumption is included here.

Facts & Assumptions

Given: Countable Choice, finite measurable positive-volume source and arrival partitions of the periodic box, the exact characteristic flow, and nonnegative source averages.

[F1]

Characteristic-cell overlap remapping for a conservative density: Di=Φ−1(Ai), ρ‾i=∣Di∣−1∑j∣Di∩Cj∣ρjn, and ρin+1=(∣Di∣/∣Ai∣)ρ‾i.

[F2]

Overlap remapping is an L2 projection, conservative and positive: under its stated Countable Choice and finite partition hypotheses, the overlap averages are nonnegative for nonnegative source values and satisfy ∑i∣Di∣ρ‾i=∑j∣Cj∣ρjn.

[A1]

The Axiom of Countable Choice (ACω): every countable family of nonempty sets has a choice function; this axiom is assumed by the overlap theorem and by the local departure-volume lemma.

[F3]

A periodic C1 characteristic map is an orientation-preserving diffeomorphism: the exact flow is a diffeomorphism on the periodic lift and hence a bijection on the torus.

[F4]

Characteristic departure cells are measurable and have positive finite volume: under Countable Choice, the departure cells form a measurable partition and each has positive finite volume.

Proof

1.1givenA1F1F2F3F4algebra

By [F3] the characteristic map is bijective on the torus, and [F4] makes the departure cells a measurable positive-volume partition. Thus the source and departure partitions meet the finite-partition hypotheses of [F2]. Applying [F2] to the source values in [F1] gives ρ‾i≥0 for every i and the departure-mass identity ∑i∣Di∣ρ‾i=∑j∣Cj∣ρjn. The Countable Choice assumption [A1] is exactly the axiom branch required by [F2] and [F4].

2.1givenF1F4step 1.1algebra∎

Since [F4] gives ∣Di∣>0 and each arrival cell has ∣Ai∣>0, [F1] scales the nonnegative ρ‾i by the positive ratio ∣Di∣/∣Ai∣, so ρin+1≥0. Also ∣Ai∣ρin+1=∣Di∣ρ‾i term by term. Summing this equality and substituting the identity from step 1.1 proves the asserted total mass equality.

Remarks

The batch-63 definition and theorem bodies have been inspected. The theorem assumes Countable Choice and finite measurable partitions with positive cell volumes; the departure-volume lemma supplies measurability and positive volume for {Di}, while the periodic grid supplies those properties for {Cj}. The cited PE17 carriers are drafts, and this Step 1 contract records mathematical and dependency readiness only; it does not claim independent approval.

5 · Examples, counterexamples and false statements

None yet.

Sources