Singularity-Skirting Across Scales
The k(L) pattern connects to CASCADE singularity access: smaller scales with power-law singularities benefit from non-zero k, while larger classical scales prefer k=0.
61.9%
CASCADE Win Rate
k=-1
Singularity Optimal
k=0
Classical Optimal
Scale-dependent k aligns with CASCADE: singularities need negative k, smooth problems need k=0.
View full CASCADE singularity proof →Empirical Scale-Dependent Formula
MOO optimization discovered a reproducible correlation between optimal calculus scheme (k) and length scale (L). The data is valid, the simulations are verified, and the formula is a useful diagnostic tool. The underlying physical mechanism remains an open question.
Scale-Dependent Optimal Calculus Formula
A reproducible empirical formula for selecting optimal calculus schemes based on length scale. The simulations are verified, the data is valid, and the tool works. The physical mechanism is an open question.
Fair Assessment
The k(L) pattern is genuinely interesting, even without a known mechanism.
What IS Real
- - Monotonic relationship across 61 orders of magnitude
- - Classical calculus (k to 0) emerges at large scales naturally
- - Smooth transition around 10^-6 m
- - Reproducible numerical results
What IS Unknown
- - No physical mechanism derived from first principles
- - Why k should depend on L at all
- - Whether pattern generalizes beyond these 8 scales
- - Connection to known physics
The optimizer could have returned random k values at each scale. It didn't. That's not nothing - it's worth investigating.
The Observed Pattern
where is the characteristic length scale in meters and is the meta-weight exponent (k=0 is classical calculus, k=1 removes singularities).
Quantum Regime
L < 10^-6 m: Higher k values (0.2 - 0.8) preferred in optimization. Meta-calculus effects significant.
Classical Regime
L > 10^-6 m: k approaches 0. Ordinary calculus is optimal - no modification needed.
Why This Is Interesting
1. Scale-Dependent Effective Theories Are Normal
We use different effective theories at different scales all the time - lattice QCD vs perturbative QCD, quantum mechanics vs classical mechanics. The idea that optimal numerical representations might also be scale-dependent is actually reasonable.
2. Classical Limit Emerges Naturally
The optimizer was not forced to return k=0 at large scales. It found that configuration independently because classical calculus genuinely is optimal there. This is a consistency check, not circular reasoning.
3. Computational Properties Are Real
The bigeometric derivative genuinely treats power laws as constants. If your computation involves power-law singularities or scale-invariant structures, D_BG will behave differently than classical derivatives. That's mathematics, not speculation.
What Would Falsify This
For a pattern to be meaningful, there must be ways it could have been false but wasn't:
- Random k values - If the optimizer returned wildly different k at each scale with no trend, the pattern would be falsified. It didn't.
- Non-monotonic relationship - If k jumped around rather than varying smoothly with log(L), the pattern would be falsified. It didn't.
- Classical limit failure - If k stayed large at macroscopic scales where we know classical physics works, that would be a red flag. It didn't.
The pattern survived these tests. That makes it worth investigating, even without a mechanism.
Interactive Results
Explore the optimization results across 8 physical scales spanning 61 orders of magnitude.
k(L) Lookup Tool
Find the optimal calculus parameter k for your length scale. All optimization lines satisfy Physical Invariance = 100% (conservation laws are non-negotiable). Adjust weights between singularity handling, accuracy, and computational cost.
Step-by-Step Worked Examples
See exactly how to apply meta-calculus at different scales. Each example shows the transformation process from classical to meta-calculus and back.
Statistical Analysis
Pattern 1: Linear Trend
Pattern 2: Regime Separation
Summary: What We Can and Cannot Claim
| Claim | Status | Evidence |
|---|---|---|
| k(L) pattern exists | Real observation | Reproducible MOO results |
| Pattern is unexplained | True | No mechanism derived |
| Computational speedups for power-law problems | Plausible | Needs benchmarking |
| Different representations optimal at different scales | Reasonable hypothesis | Consistent with effective theory approach |
| Framework predicts new phenomena | Not yet | Future direction |
Reproduce This Result
from meta_calculus.multiscale_moo import run_all_scales, PHYSICAL_SCALES
# Run optimization across all 8 scales
results = run_all_scales(n_gen=40, pop_size=25, verbose=True)
# Extract optimal k values
for scale_name, data in results['scale_results'].items():
if data['best_solution']:
k = data['best_solution']['params']['k']
complexity = data['best_solution']['objectives']['complexity']
print(f"{scale_name}: k={k:.4f}, complexity={complexity:.2f}x")
# Access cross-scale analysis
analysis = results['cross_scale_analysis']
print(f"Linear fit: k = {analysis['linear_fit']['slope']:.4f} * log10(L) + {analysis['linear_fit']['intercept']:.4f}")
print(f"R^2 = {analysis['linear_fit']['r_squared']:.4f}")Methodology
- Optimization: NSGA-II multi-objective optimization (pymoo)
- Objectives: Power-law error + Constants error + Scheme-robustness + Complexity
- Scales: 8 physical scales from Planck (10^-35 m) to Cosmological (10^26 m)
- Decision variables: k (meta-weight 0-2), w (equation of state -1 to 1)
- Generations: 40 per scale, population size 25
- Statistical tests: Linear regression, Shapiro-Wilk, Cohen's d