ERG volume

ERG volume

GRG Sphere — Points • Surface • ERG Volume • Smooth

Abstract. A sphere of radius $R$ is generated as a quasi-uniform point set. With sampling step:

$$ h_{\max}(\kappa,\varepsilon)\;=\;2\sqrt{\frac{\varepsilon}{\kappa}}, \qquad \kappa=\frac{1}{R}, $$

the reconstructed surface is perfect at scale $\varepsilon$ (geometric error $\le \varepsilon$); area and ERG volume converge with $O(h^2)$ error.

1) Definitions

  • Resolution $\varepsilon$: admissible indistinguishability threshold.
  • Step $h$: distance between nearby surface samples.
  • Curvature $\kappa$: for a sphere, $\kappa = 1/R$.
  • Perfect at scale $\varepsilon$: Hausdorff distance (ideal ↔ reconstructed) $\le \varepsilon$.

Geometric reminder (sagitta of an arc of radius $R$):

$$ s \;\approx\; \frac{h^{2}}{8R} \;=\; \frac{\kappa h^{2}}{8} $$
$$ \Rightarrow\quad h_{\max} \;=\; 2\sqrt{\frac{\varepsilon}{\kappa}} \;=\; 2\sqrt{\varepsilon R}. $$

2) Points → Surface → ERG volume

2.1 Points (quasi-uniform sampling)

  • Spherical Fibonacci lattice: simple and isotropic.
  • Icosphere (subdivided icosahedron): regular connectivity.

Point count (triangles near-equilateral, area $(\sqrt{3}/2)\,h^2$):

$$ N \;\approx\; \frac{4\pi R^{2}}{(\sqrt{3}/2)\,h^{2}} \;=\; \frac{2\pi}{\sqrt{3}}\,\frac{R}{\varepsilon}. $$

It grows linearly with $R/\varepsilon$.

2.2 Surface (closed, oriented mesh)

Triangulate coherently on the spherical manifold. Face normal: $\mathbf{n}_f \propto (p_j-p_i)\times(p_k-p_i)$.

Discrete area:

$$ A \;=\; \sum_{\triangle ijk} \frac{1}{2}\,\big\|\, (p_j-p_i)\times(p_k-p_i)\, \big\|. $$

2.3 ERG volume (from closed surface)

With an oriented closed mesh (tetrahedra w.r.t. the origin):

$$ V_{\mathrm{ERG}} \;=\; \frac{1}{6}\sum_{\triangle ijk}\big((p_i\times p_j)\cdot p_k\big). $$

Convergence: $A$ and $V_{\mathrm{ERG}}$ approach the ideal values with $O(h^2)$ error.

3) Smooth$_{\varepsilon}$ (when and how)

Goal: stabilize normals and reflections without exceeding $\varepsilon$.

  1. Local Taubin/MLS filter (forward/backward pass to avoid shrinkage).
  2. Projection onto the sphere ($\lVert v\rVert \to R$).
  3. Check: Hausdorff (pre/post) $\le \varepsilon/2$.
  4. Smoothed normals from local MLS fit ($\ge C^{1}$ continuity).

When it’s not needed: if you only measure area/ERG volume and faceting is already below the $\varepsilon$-scale threshold.

4) Algorithm (concise)

input: R, ε
κ ← 1/R
h_max ← 2*sqrt(ε/κ)       # = 2*sqrt(εR)
N    ← round((2π/√3) * R/ε)

P ← fibonacci_sphere(N)    # or icosphere(h_max)
T ← triangulate_on_sphere(P)
orient(T)                  # outward normals

A ← sum_areas(T)
V_ERG ← tetra_volume(T)

if need_smooth_normals:
    P' ← taubin_mls(P, T)  # controlled smoothing
    P' ← project_to_sphere(P', R)
    assert hausdorff(P, P') ≤ ε/2
    recompute_normals(P', T)
output: (P, T, A, V_ERG, normals)

5) Checks & validation

  • Hausdorff distance (ideal ↔ mesh) $\le \varepsilon$.
  • Triangle quality (avoid extreme angles).
  • No self-intersections.
  • $O(h^{2})$ convergence for area and ERG volume.

6) Why it matters

The sphere is an ideal testbed: constant curvature and closed-form $A,V$. The chain points → surface → ERG volume → smooth at scale $\varepsilon$ sets the general GRG/ERG procedure for solids.

7) Figures (inline SVG)

Sagitta geometry: s ≈ h^2/(8R) Arc of radius R, chord of length h, sagitta s; all lengths in the same units. R O s | ⟂ h
F3 — Sagitta relation used to bound $h_{\max}$.
Quasi-uniform points on a sphere Wireframe sphere with dots to suggest Fibonacci vs icosphere distributions.
F1 — Point distribution (Fibonacci vs icosphere idea) at equal $h$.

8) FAQ

Is “perfect” absolute?
No: it is perfect at scale $\varepsilon$. Details $<\,\varepsilon$ are not guaranteed.

Do we always need smoothing?
No: needed for normals/optics stability; otherwise optional.

Fibonacci or icosphere?
Both work: Fibonacci is simple and isotropic; icosphere offers regular connectivity.

How do I choose $\varepsilon$?
Based on use: measurement accuracy, rendering, fabrication, etc.

ERG Quick Calculator ERG grid icon Concentric arcs with dyadic ticks representing ERG refinement.

This compact tool computes the quarter-sampling step Δu and the grid size K from the dyadic level b, previews the uk values, and exports the grid to CSV.

Inputs

  • Dyadic level b ∈ ℕ≥0 (recommended range: 0–18)
  • Optional: recent estimates V2b-1 and V2b to check the dyadic stopping rule
\[ \Delta u \;=\; 2^{-(b+2)}, \qquad K \;=\; \left\lfloor \frac{1}{\Delta u} \right\rfloor \;=\; 2^{\,b+2}. \]
\[ \text{Stop if}\quad \bigl|V_{2^{b}} - V_{2^{b-1}}\bigr| \le \varepsilon. \]

Calculator

Results

  • Δu = — (exact: —)
  • K = — (i.e., number of grid points)
  • N = — (turns-first level, N = 2b)
Preview of u-grid uk = k · Δu

First 8: —

Last 8: —

—

Practical checklist

  • Use dyadic refinement: increase b until the dyadic difference falls below ε.
  • Quarter-sampling is assumed: Δu = 2-(b+2).
  • Grid is deterministic; CSV can be versioned for reproducibility.
Paradigm Shift

Paradigm Shift

A Constructive View of Radial Geometry

Generative Radial Geometry studies measures through explicitly defined radial constructions. Its current reference is a finite binary sector framework with internal chord and external tangent elements.

Start with Definitions

Choose the radius, a full Turn, and a sector resolution. Define the geometric elements before choosing a kernel or discussing accuracy.

Separate Three Questions

  • Construction: which elements and operations define the object?
  • Measurement: which kernel produces the finite estimate?
  • Verification: under which assumptions do bounds and convergence hold?

Current Results

Read the current formulation and analytical verification. The results concern regular symmetric sectors in exact arithmetic. Computational efficiency and extensions require separate evidence.

Research History

Earlier projection, Cartesian trapezoidal, and Turns approaches remain documented. They should be compared within their own scope. This page replaces the earlier slogan-based presentation with a statement of the constructive method and its verification requirements.

GRG Speed & Hardware

GRG Speed & Hardware

GRG Computational Cost and Hardware

Binary sector indices and power-of-two scaling motivate investigation of digital implementations. They do not establish a performance advantage for the complete calculation.

Define the Task

Kernel evaluation, generation of radial nodes, drawing a raster boundary, filling a disk, and building a volume are different tasks. Their complexity depends on the output representation and implementation.

Count Complete Costs

Include generation or retrieval of geometric ratios, arithmetic precision, divisions and square roots, precomputation, memory access, and output generation. A final multiplication alone does not describe the complete cost.

Hardware Directions

Possible directions include fixed-point arithmetic, lookup tables, vectorisation, parallel execution, and programmable hardware. Each requires an implementation and measurements; their availability is not a claim that GRG already outperforms alternatives.

Evidence Required

Use the benchmark protocol and publish comparisons at equal achieved accuracy. Test rounding, cancellation, overflow, and refinement limits. State what is geometric identity and what is numerical approximation.

Current Status

The current formulation supplies geometric kernels and accuracy proofs. This page makes no unsupported claim of speed, stability, throughput superiority, or three-dimensional scaling.

Wedge Visualization (Interactive)

Wedge Visualization (Interactive)

GRG — Wedge Visualization: Interactive Uniform vs Adaptive Discretization

This mini-demo provides concrete, reproducible numbers for wedge-stratified GRG. Adjust the controls to compare a uniform wedge count against an adaptive profile on the outer annulus, and see how certified corridor half-widths contract roughly like 1/\sqrt{W_k} for comparable ring budgets.

How the Demo Works (plain language)

  • Dyadic edges. Rings follow turns-first dyadic refinement with b∈ℕ≥0, N=2^b, and quarter sampling Δu=2^{-b-2}.
  • Corridor half-width. Each sector’s estimate has a certified interval [E^- , E^+]. The half-width is (E^+−E^-)/2; decisions stop when this width ≤ tolerance τ.
  • Field model. We synthesize an anisotropic field with mixed angular orders up to fourth order to stress the wedges; values are computed in-page so the numbers are concrete and repeatable.
$$ F(\rho,\theta)=A(\rho)\Big[1+\epsilon_2\cos 2(\theta-\theta_2)+\epsilon_3\cos 3(\theta-\theta_3)+\epsilon_4\cos 4(\theta-\theta_4)\Big], $$
$$ \textbf{Stop when}\quad \widehat{E}_b^{+}-\widehat{E}_b^{-}\le \tau,\qquad \text{with}\ \ \Delta u=2^{-b-2}. $$

Controls

Results — Per-Sector Estimates on the Outer Annulus

Hover or tap a bar to read exact values (estimate and corridor half-width). The polar plots show the directional pattern and corridor bands.

Uniform W — outer annulus sectors (bars = estimate, whiskers = corridor half-width).
Adaptive outer Wk — tighter corridors where anisotropy is richer.
Polar — Uniform W.
Polar — Adaptive Wk.

Concrete Numbers (auto-computed)

The table aggregates sector values (outer annulus) for the current settings. Replace later with CSVs from your scripts; the structure is the same.

Notes & Practical Guidance

  • When to prefer GRG. As angular richness grows or is localized, adaptive W_k exposes structure with linear O(K·W) cost and certified decisions via τ.
  • When low-order bases suffice. Near-radial scenes with mild 2-fold variation are well captured by a short angular basis; choose GRG when auditability (corridors) matters.
  • Next steps. Publish side-by-side benchmarks (Zernike, B-splines, Fourier–Bessel) with the same corridor-aware stopping rule and public scripts/datasets.
GRG — Wedge Visualization

GRG — Wedge Visualization

Wedge-Stratified GRG — Visualizing Per-Sector Results

This section visualizes the anisotropic, wedge-stratified example discussed earlier. We compare a uniform W=8 discretization against an adaptive scheme on the outer annulus \rho\in[0.7,1), where angular content is richer. The plots show per-sector estimates with certified corridor half-widths.

Setup Recap

Field with mixed angular orders up to fourth order:

$$ F(\rho,\theta)=A(\rho)\,\Big[1+\epsilon_2\cos 2(\theta-\theta_2)+\epsilon_3\cos 3(\theta-\theta_3)+\epsilon_4\cos 4(\theta-\theta_4)\Big], $$

Rings and wedges:

$$ \text{Rings }k=1..K,\ \text{wedges }w=1..W_k,\quad I_{k,w}=\int_{\Theta_w}\!\int_{u_{k-1}}^{u_k} F(\rho,\theta)\,\rho\,d\rho\,d\theta. $$
  • Rings: K=32 (dyadic edges, quarter sampling).
  • Uniform: W=8 for all rings.
  • Adaptive: inner W_k=8, mid W_k=12, outer W_k=16 (used for plots below).

Per-Sector Results on the Outer Annulus

The numbers are the same illustrative values used in the write-up; replace them with your measured outputs when available. Error bars display corridor half-widths.

Left: uniform W=8. Right: adaptive outer ring with W_k=16. Hover/tap a bar to read exact values.

Polar View (Directional Pattern)

A simple polar plot places each sector’s estimate radially. Shaded rings visualize the corridor range.

Polar plot of per-sector estimates. Left: W=8. Right: W_k=16.

Reading the Plots

  • Corridor effect: with higher angular resolution, corridor half-widths shrink (≈ 1/\sqrt{W_k} for comparable ring budgets), revealing higher-order structure.
  • Decision making: choose the smallest dyadic level b such that the corridor width is below the task tolerance \tau.
$$ \textbf{Stop when}\ \ \widehat{E}^{+}_{b}-\widehat{E}^{-}_{b}\le\tau. $$