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Interacting particle mean field and vortex blob error — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Areas of Elementary Plane Figures
- Banach-Space Differential Calculus and Banach Manifolds
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- Complexification, Realification and Real Structures
- Composite quadrature rules
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Darboux, L'Hôpital, and Taylor's Theorem
- Density Separability and Convolution in Lᵖ
- Determinants of Matrices over a Commutative Ring
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Finite Probability and the Probabilistic Method
- Foundations of the Real Numbers for Analysis
- Fubini and Change of Variables
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Global ODE and Log-density Integration Error
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Interacting particle mean field and vortex blob error
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- One-step ODE Consistency and Local Error
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Picard-Lindelöf and First-Order Ordinary Differential Equations
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Runge–Kutta Methods and Order
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Subspaces, Products, and Quotients
- Suprema and Infima
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Inverse and Implicit Function Theorems
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
The examples make the model and error assumptions concrete. In A globally Lipschitz alignment model with exact mean trajectory, the kernel has two-variable Lipschitz constant . The particle mean is constant; for , every particle approaches it by an explicit exponential trajectory, while for every particle is stationary, illustrating the exact empirical weak equation and the general Dobrushin stability convention.
A dimension-dependent lower bound for empirical W1 error uses a uniform law on the vertices of a high-dimensional cube. Counting unsampled vertices gives a deterministic lower bound on empirical error and rules out the stated uniform dimension-free empirical rate. A mixed-sign vorticity is not a probability for W1 gives a nonzero signed measure of total mass zero, explaining why probability cannot replace the signed discrepancies defined on the main page.
Softened Newtonian particles do not specify the physical collision force compares singular Newtonian relative acceleration with a smooth softened field. Smoothness at a collision changes the force model; it supplies no collision law for the singular system.
Same-sign smooth vortex density and probability blob error budget specializes to a smooth nonnegative unit-mass vorticity and distinct equal-weight initial particles. Under its Countable Choice, common smooth force-residual, and RK4 tube assumptions, including numerical grid states, admitted stages from both exact and numerical starts, and defined one-step maps at every grid step, its probability coupling estimate separates initial empirical error, blob regularization, Simpson force quadrature, and fourth-order time error. The initial coupling need only approach the infimum. The conclusion remains conditional at fixed blob width and makes no uniform singular-limit claim.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Softened Newtonian particles do not specify the physical collision force
Example
For two Newtonian point masses in with positive constants absorbed into , the unsoftened relative acceleration is for , where is Euclidean. A softened model uses The softened field is smooth at a collision, but it defines a different dynamical model.
Facts & Assumptions
Given: , , and the two displayed force laws.
The norm is Euclidean (The Euclidean inner product on ).
Verification
Given: The unsoftened and softened force formulas in the Example.
For , their scalar denominators are and , which differ because . Thus for every nonzero .
At , the unsoftened formula is undefined, whereas the softened formula gives . The denominator is smooth and strictly positive, and is smooth for ; hence the softened field is on all of . Its smooth extension therefore supplies a convention for its own regularized model, not a collision law for the singular Newtonian system.
A dimension-dependent lower bound for empirical W1 error
Example
Let with , set , and let be uniform on its vertices. For any empirical measure obtained from samples , the 1-Wasserstein error has the deterministic lower bound Thus no dimension-free bound can hold uniformly over all dimensions and all empirical samples.
Facts & Assumptions
Given: The uniform probability on , an integer , and any list of sample vertices .
is the infimum of mean Euclidean transport costs over probability couplings (The coupling definition of the 1-Wasserstein distance).
Verification
Given: The data in the Example.
Let be the empirical support. It contains at most vertices, so . Distinct vertices of have Euclidean distance at least one.
In any coupling of to , every point of must send its mass to a point of , at cost at least one per unit mass. Hence every coupling costs at least , and taking the infimum in [F1] proves the bound. If , it is at least ; choosing arbitrarily large and then such an rules out a uniform dimension-free bound.
A mixed-sign vorticity is not a probability for W1
Example
On , let . It is a nonzero finite signed measure of total mass zero and total variation two. The probability in The coupling definition of the 1-Wasserstein distance cannot be applied to .
Facts & Assumptions
Given: The signed measure of the Example.
is defined only for probability measures with finite first moment (The coupling definition of the 1-Wasserstein distance).
is the dual supremum over functions bounded by one with Lipschitz constant at most one (Bounded-Lipschitz dual discrepancy for finite signed measures).
Total variation is the supremum over measurable partition sums (The total variation |nu|(E) from countable measurable partitions).
The reverse triangle inequality gives for the Euclidean norm (Cauchy-Schwarz with its equality case, the triangle inequality for , the parallelogram law and polarisation).
Verification
Given: .
Among any countable partition, at most two cells have nonzero signed mass, each of magnitude one; the partition into the two singleton atoms and their complement attains the sum two. Thus by [F3], while its signed mass is . Therefore is neither a nonnegative probability measure nor normalizable by its net mass, so the domain condition in [F1] fails.
The function is bounded by one and 1-Lipschitz by [F4], with and . Hence , so . This finite signed dual quantity remains meaningful by [F2], whereas probability is undefined.
A globally Lipschitz alignment model with exact mean trajectory
Example
For on , take . For , each particle is pulled toward the current mean position; for , every particle is stationary. It is globally Lipschitz, and its particle trajectories are explicit.
Facts & Assumptions
Given: , , and initial positions .
A globally Lipschitz interaction kernel uses the two-coordinate bound in Globally Lipschitz mean-field interaction kernel.
The equal-probability particle system is (Equal-probability mean-field and weighted signed vortex particle systems).
The empirical particle measure satisfies the exact weak equation (Exact weak mean-field identity for empirical particle measures).
General characteristic probability solutions obey the stability estimate (Dobrushin stability in 1-Wasserstein distance for mean-field flows).
Verification
Given: The data in the Example and .
For all , , so the global Lipschitz constant may be taken as in [F1].
Substitution into [F2] gives , where . Summing over yields , and hence For , the formula tends to the constant mean as ; for , it equals . Thus the empirical measure is an exact weak solution by [F3]; for probability solutions the stability factor is by [F4].
Same-sign smooth vortex density and probability blob error budget
Example
Assume Countable Choice. Let be nonnegative with mass one, and suppose the smooth Euler flow satisfies , , and , with . Approximate it by distinct initial positions with equal weights , define , and let .
Let with and , let be even integers at least two, and put and . For every time and force location , let and assume the uniform bounds Define and where are the composite Simpson rules in the two rectangle coordinates. By Tensor composite Simpson error for a two-dimensional force integral, uniformly. Assume this common residual is smooth in , and let the exact particles solve . Assume the resulting nonautonomous field is jointly on an open time-state domain , with derivatives through order four bounded on a common compact convex tube containing the exact paths and RK4 stages. For an integer with , let be their RK4 approximations from the same initial nodes, with all paths and stages in that tube. Define . If is the coefficient from Fourth-order global error for RK4 on a regularized vortex particle tube for this , then
The RK4 tube hypothesis includes all numerical grid states and every stage generated at each grid step from both the exact start and the numerical start, and both one-step maps are defined, as required by Fourth-order global error for RK4 on a regularized vortex particle tube.
Facts & Assumptions
Given: The smooth probability vorticity and Euler flow; distinct initial particle nodes; fixed ; a common smooth spatial quadrature field with residual ; and the RK4 tube and derivative hypotheses.
Assume Countable Choice for the polar-coordinate kernel-consistency estimate (The Axiom of Countable Choice ()).
is the coupling infimum for finite-first-moment probability measures (The coupling definition of the 1-Wasserstein distance); the product coupling has finite cost (The coupling infimum is finite for finite-first-moment probabilities).
Kernel mollification gives force defect at most (L1 consistency of the mollified Biot-Savart kernel).
Tensor Simpson gives the uniform residual (Tensor composite Simpson error for a two-dimensional force integral).
Given characteristic flows with force defects , every initial coupling transports with cost at most (Probability W1 stability under bounded force defects).
RK4 on the controlled smooth particle tube has maximum Euclidean state error at most (Fourth-order global error for RK4 on a regularized vortex particle tube).
The mollified kernel has a finite global Lipschitz constant (The mollified Biot-Savart kernel is smooth and globally Lipschitz).
Verification
Given: The data in the Example and the estimate for the initial empirical probability.
Write the exact Euler characteristic velocity as the mollified interaction plus residual , whose norm is at most by [F2]. The quadrature particle field is the mollified interaction plus a common residual of norm at most by [F3]. Fix any initial coupling with cost , where . Transport it by the Euler flow and the exact quadrature-force particle flow. By [F4], the resulting coupling has cost at time at most
Use the same initial particle label in the exact quadrature-force path and the RK4 path. The RK4 state error in [F5] is Euclidean on the full particle state, so each particle-block displacement is at most . Since the initial nodes are distinct, partition the coupling by its second coordinate and label each part by its unique particle index. Push this labelled coupling through the exact Euler flow in the first coordinate and the RK4 particle map in the second. The resulting coupling from the Euler law to the RK4 empirical law has cost at most the step 1.1 bound plus . Thus it bounds their . Since was arbitrary, let to obtain This same-sign estimate uses probability transport and has no AC hypothesis; it makes no uniform claim as .