Simulation Analytics & Cross-Model Metrics
Spine Position
Mathematics Root · Simulation Telemetry · Cross-Model Analytics
Status Boundary
This work is part of the authorial PHYSICA / Science of Fabric Reality corpus. It is presented as a research framework, formalization target, computational model, or theoretical synthesis unless explicitly marked otherwise. It is not presented as accepted physics, external consensus, or experimentally confirmed science.
1. Analytics & Status Boundaries
SIMULATION BOUNDARY
The computations displayed on this page represent a Simulation Candidate (M4-SIMULATION classification). This is an algorithmic execution stress-test of internal mathematical invariants.
DO NOT interpret this output as empirical confirmation or formal theorem proof. The environment is strictly a computational model.
INTERPRETATION BOUNDARY
Any metrics or timeseries data derived from this simulation are strictly confined to the defined parameter space and cannot be generalized to physical reality without corresponding formalization and review (S5 classification).
2. Purpose & Coverage
The Simulation Analytics layer unifies computational simulation outputs under a standardized evaluation schema so prototype models become mathematically comparable.
This dashboard currently ingests telemetry from:
recursive-observer-knot-caclosure-field-dynamicskp-field-propagationrecursive-intelligence-network
3. Diagnostic Health
4. Common Metric Ontology
The cross-model matrix uses an abstraction ontology to map disparate state variables into standard bounds.
Ontology Coverage
5. Cross-Model Matrix
Cross-Model Matrix
| Simulation Target | Class | Runs | Diagnostic Status | Violations | Available Telemetry |
|---|---|---|---|---|---|
| closure_field_default | Unknown | 1 | stable | 0 | connectivity, final_mean_density, final_closure_score |
| closure_field_stress | Unknown | 1 | stable | 0 | final_mean_density, final_closure_score |
| kp-field-propagation | Field | 2 | bounded failure | 1 | mean_amplitude, mean_phase, resonance_count, instability_count, coupling_energy, damping_loss, boundary_support_ratio |
| kp_field_propagation_default | Unknown | 1 | stable | 0 | None |
| kp_field_propagation_stress | Unknown | 1 | stable | 0 | None |
| recursive-intelligence-network | RecursiveSystem | 2 | bounded failure | 2 | network_coherence, signal_retention, feedback_density, knowledge_accumulation, continuation_ratio |
| recursive_observer_knot_ca_default | Unknown | 1 | stable | 0 | None |
| recursive_observer_knot_ca_stress | Unknown | 1 | stable | 0 | None |
| trace_reciprocity_default | Unknown | 1 | stable | 0 | None |
| trace_reciprocity_stress | Unknown | 1 | stable | 0 | None |
6. Interpretation Limits
The absence of violations in a simulation run indicates structural stability within the programmed bounds, not proof of the underlying hypothesis.
7. Canonical Continuations
| Direction | Target Resource | Purpose |
|---|---|---|
| Simulation Lab | Canonical Simulation Atlas Hub | 15-point typed simulation contract, solver families, and |
| Simulation Bridge | Formalization ↔ Simulation Bridge | Diagnostic mapping between Lean 4 proofs and discrete numerical solvers |
| Proof Gateway | Lean 4 Formalization Roadmap | 28 machine-verified theorem records and lemma dependency DAGs |
| Falsifiability Map | Falsifiability Index & Negative Theorems | Quantitative failure criteria and singular Fisher information bounds |