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https://github.com/allaunthefox/Research-Stack.git
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94 lines
1.9 KiB
Markdown
94 lines
1.9 KiB
Markdown
# Custom Stack Modules
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These modules adapt the Newtonian Superfluid Simulation into the broader semantic-mass / geometric ontology stack.
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## Source model
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The repository simulation is particle-based. The README describes a fluid model of particles interacting through attraction, repulsion, and spin-like/tangential dynamics, where force-balance changes produce dynamic micro-to-macro patterns.
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The current `simulation.py` implements a finite 2D particle system with:
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```text
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N_particles = 350
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box_size = 50.0
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dt = 0.04
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k_gravity = 100.0
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k_repel = 100.0
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softening = 1.2
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R_max = 10.0
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damping = 0.95
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max_vel = 12.0
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```
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with local attraction and repulsion:
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```text
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F_grav = k_gravity / (r^2 + softening)
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F_repel = -k_repel / (r^4 + 0.1)
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```
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## Added modules
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### `superfluid_semantic_adapter.py`
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A Python adapter that runs finite probes and exports dimensionless semantic diagnostics:
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```text
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mass_number
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semantic_density
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torsion
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kinetic_pressure
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basin_strength
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receipt_coverage
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gate
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```
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It also exports Q16.16-compatible values for browser/Lean/Wasm bridge use.
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Run:
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```bash
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python custom_stack/superfluid_semantic_adapter.py
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```
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### `SuperfluidSemanticKernel.lean`
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A Lean-side fixed-point gate kernel defining:
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```text
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SuperfluidSemanticState
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GateScope
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routeAdmissible
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superfluid_mass_q16
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superfluid_density_q16
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superfluid_torsion_q16
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superfluid_basin_q16
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superfluid_gate_scope
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```
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## Boundary
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These modules do **not** claim:
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```text
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semantic mass is SI physical mass
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the particle simulation is a literal validated superfluid model
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visualization proves ontology
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high mass_number proves a claim
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```
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They provide a finite adapter layer:
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```text
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particle dynamics -> dimensionless semantic diagnostics -> custom ontology/render stack
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```
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## Intended next bridge
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```text
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simulation.py / adapter
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-> semantic state JSON or Q16.16 values
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-> WebGPU ontology renderer
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-> Lean/Wasm receipt gate
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-> FAMM / Inverted FAMM route memory
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```
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