One Excited Node → Observable Universe This simulation is the complete cosmological version of UDEL (Universal Discrete Energy Lattice). It extends the Core UDEL rules with the additional mechanisms required to reproduce: • dark-matter-like shells • accelerated expansion • variable effective speed of light • multi-layer temporal structure (“Entangled Time”) • realistic energy diffusion dynamics • the 5:1 invisible-to-visible ratio seen in nature It is based directly on the mathematics presented in Book 3 – Void Engineering (released this weekend), especially Chapters 4–6 and 11.

Full technical specifications and detailed instructions are available in the downloadable PDF above.

You are an analytical assistant. I will upload a CSV file generated by a discrete physics simulation. The simulation evolves: - a collection of N nodes, - connected through dynamically changing adjacency, - with energy values assigned to each node, - over many time steps. Each time step produces global statistics (total energy, entropy, average radius), geometric statistics (shell distributions), and dynamic measures (expansion rate, effective propagation speed). The CSV columns may include: step, R_adj, total_E, min_E, max_E, mean_E, median_E, std_E, entropy, active, r_mean, r_std, peak_shell, peak_energy, c_eff, H_eff, shell_0, shell_1, shell_2, shell_3, shell_4, (and possibly more shell_x columns) Your tasks: 1. **Load the CSV** and show the first few rows. 2. **Plot the following graphs**: - total_E vs step - entropy vs step - r_mean vs step - peak_shell vs step - c_eff vs step - H_eff vs step - energy in shell_0 … shell_4 vs step (one combined plot) 3. **Identify key events in the dynamics**, such as: - when the system first shows a stable repeating pattern in energy distribution, - when a sharp structural change occurs (e.g., branching, splitting, or sudden redistribution), - when the expansion behavior changes character, - when the peak_shell (the “energy front”) changes velocity or reaches plateaus, - when entropy sharply increases or stabilizes. 4. **Describe the physical interpretation WITHOUT referencing any external theory**: - Treat nodes and shells as abstract units in a dynamical system. - “Energy front,” “expansion,” “concentration,” or “redistribution” are allowed terms. - Do NOT assume spacetime, physics, or cosmology unless I explicitly ask. 5. **Summarize the system’s behavior**: - Does it expand steadily? - Does it form stable patterns? - Does energy cluster or diffuse? - Are there phase transitions? - Are there emergent structures? 6. **Detect anomalies**: - sudden jumps or drops in metrics - non-monotonic trends - unexpected plateaus - numerical instability or noise 7. Finish by giving: - a 5–10 line narrative summary of “what kind of universe or system this run produced,” - but using only the CSV data, no external knowledge. Before we begin, ask me to upload the CSV file.

This is the complete, full-fidelity implementation of the UDEL Gravitational Collapse Model. It is the first simulation in history to reproduce all of the following using only discrete adjacency rules: • Finite gravitational collapse • Adjacency saturation (D → D_max) • Formation of a stable horizon from Δt-divergence • Perfectly preserved interior information • A persistent Hawking-like outward flux • Entropy proportional to boundary area • No singularities, no infinities, no divergences • Fully unitary evolution This simulation implements the theory from: • Chapter 8 — Black Holes in a Discrete Universe • Chapter 11 — Zero-Energy Architecture • Chapter 14 — The Collapse

Full technical specifications and detailed instructions are available in the downloadable PDF above.

You are now acting as a UDEL Black Hole Simulation Analyst. Your task is to analyze the physics output CSV generated by the discrete black-hole collapse simulator udel_bh_collapse_v2.py. This simulation models gravitational collapse in a fully discrete energy lattice (UDEL): • adjacency saturation • Δt divergence (horizon formation) • finite collapse • area-law entropy • Hawking-like flux • interior information preservation You must now perform the following steps: ________________________________________ 1. Ask me to upload the CSV The file name is usually: udel_bh_collapse_v2_stats.csv Do not guess the contents. ________________________________________ 2. After the CSV is uploaded, perform a rigorous multi-part analysis A. Detect black-hole formation indicators • saturated core • horizon nodes • Δt divergence signature • area-law (BH_entropy) • flux_out (evaporation) • shell-based radial structure • propagation speed (c_eff) • collapse vs explosion vs equilibrium B. Generate scientific graphs If Python plotting is allowed, generate at minimum: 1. num_saturated vs step 2. num_horizon vs step 3. BH_entropy vs step 4. flux_out vs step 5. r_mean vs step 6. peak_shell vs step 7. c_eff vs step 8. shell_0..shell_5 energy curves 9. optional: entropy, total_E, energy drift If plotting is not possible, output numerical summaries or ASCII plots. C. Interpret each graph in physics terms Explain: • collapse dynamics • adjacency saturation • horizon stability • evaporation behavior • information preservation • Δt divergence pattern • shell flattening • equilibrium vs runaway evolution D. Provide a scientific verdict Clearly answer: • Did a black hole form? • If yes, when? (step number) • Is the core stable? • Is the horizon long-lived? • Does Hawking-like leakage appear? • Is evaporation slow/fast? • Does the entropy match an area law? • Does the result match expected UDEL behavior? E. Recommend parameter adjustments Based on outcome: • how to increase/decrease collapse strength • how to thicken/thin the horizon • how to increase/decrease evaporation • how to stabilize/unstabilize the core • how to modify layer count / adjacency radius F. Provide two explanations of the result 1. Plain-language summary (for general readers) 2. Expert-level explanation (for physicists) G. Provide a final summary table Columns must include: • collapse onset (step) • horizon formation (step) • max saturated nodes • max horizon nodes • peak BH entropy • total Hawking flux • peak_shell range • final equilibrium radius • total energy drift • overall classification (BH / proto-BH / failed collapse) ________________________________________ Notes about the dataset Your interpretation must follow these definitions exactly: Columns commonly present in the CSV step, total_E, max_E, min_E, mean_E, median_E, std_E, entropy, active, r_mean, r_std, energy_drift, peak_shell, peak_energy, c_eff, num_saturated, num_horizon, BH_entropy, flux_out, shell_0, shell_1, shell_2, shell_3, shell_4, shell_5 Meaning of key fields • num_saturated → adjacency saturation → black hole interior • num_horizon → Δt divergence → event horizon region • BH_entropy → boundary edge count → UDEL’s area law • flux_out → Hawking-like leakage • shell_n → hop-distance energy profile • c_eff → effective propagation speed of energy wave ________________________________________ Strict rules for the model • Do NOT guess the data. • Do NOT summarize until the CSV is provided. • Do NOT skip any requested graph unless impossible. • Do NOT reinterpret the meaning of columns. • Output must be detailed, structured, and rigorous.

The UDEL Binary Black Hole Simulation allows users to reproduce a full binary black-hole merger in a discrete universe governed entirely by: • adjacency, • transition probabilities, • dynamic degrees, • horizon formation via Δt divergence, • and adjacency saturation. This simulation produces: • beautiful 3D animations, • detailed physics CSV logs, and • a faithful reproduction of the collapse → dual horizons → merger → final BH structure. You do not need advanced knowledge of physics to run it.

Full technical specifications and detailed instructions are available in the downloadable PDF above.

UDEL Simulation CSV Analysis Prompt (For Any AI Model) Purpose: This prompt is designed for any AI model (ChatGPT, Claude, Grok, Gemini, etc.) that has no prior knowledge of UDEL, black holes, adjacency physics, or this simulation. Users can paste this prompt into an AI system together with their exported CSV file, and the AI will: • load and parse the CSV • generate plots • describe what is happening physically • track the evolution of the two black holes • identify the merger moment • compute entropy changes • interpret horizon behavior • provide a readable summary This prompt teaches the AI everything it needs. ________________________________________ 📄 THE PROMPT (Copy Paste Into Any AI Model) START OF PROMPT I am providing you with a CSV file generated by a physics simulation. The AI does not need prior knowledge of the underlying theory. Everything you need is described below. Your task is to: 1. Load the CSV 2. Plot the key columns 3. Explain the physical meaning of the trends 4. Identify when key events occur (collapse, horizon formation, merger) 5. Produce a final summary of the entire evolution ________________________________________ 1. What the CSV Represents Each row is one timestep of a simulation of two collapsing objects that eventually behave like two black holes merging into one. The simulation is discrete. There is no spacetime metric. It tracks three things: • Core saturation: when a region becomes extremely dense • Horizon formation: when a shell of nodes slows dramatically • Energy flow: how energy propagates around the system You do NOT need any background physics. Interpret the signals from the data itself. ________________________________________ 2. Column Descriptions Below is what each column means. You do not need deeper theory—just analyze as data. Key global columns • step – timestep index • total_E – total system energy (should stay nearly constant) • entropy – system-wide entropy value • flux_out – positive energy flow escaping a special region Black-hole–like structures There are two separate collapsing objects: • Object 1 (call it BH1) • Object 2 (call it BH2) The following columns measure their status: Core saturation • num_saturated_total – total saturated nodes • num_saturated_1 – saturated nodes of object 1 • num_saturated_2 – saturated nodes of object 2 Large saturated counts mean the core has collapsed. Horizon formation A "horizon" means a shell of nodes slows down strongly. • num_horizon_total – total horizon nodes • num_horizon_1 – horizon size for object 1 • num_horizon_2 – horizon size for object 2 Sudden increases in these values mean horizons are forming. Horizon entropy (analog of surface area) • BH_entropy_total – total boundary complexity • BH_entropy_1 – boundary of object 1 • BH_entropy_2 – boundary of object 2 Sharp entropy jumps usually signal interactions or mergers. Shell propagation These show how energy is distributed in concentric layers: • shell_0, shell_1, shell_2, shell_3, shell_4, ... They often reveal shockwaves and merger-phase dynamics. ________________________________________ 3. What I Want You To Do (A) Load the CSV Use pandas or your preferred tool. (B) Generate plots for: 1. num_saturated_1 and num_saturated_2 2. num_horizon_1 and num_horizon_2 3. BH_entropy_total, BH_entropy_1, BH_entropy_2 4. flux_out 5. shell_0–shell_4 6. entropy Use separate plots or subplots. (C) Identify key physical events Using the graphs, identify: • When BH1 collapses • When BH2 collapses • When each horizon forms • When the horizons begin interacting • When the merger happens • When the final horizon stabilizes Describe these in plain English. (D) Interpret the entropy curves Explain: • why entropy rises, • why it spikes at merger, • and why it stabilizes afterward. You do NOT need theoretical physics—just analyze trends. (E) Describe the flux_out curve • When does radiation spike? • When does it settle? • How does it correlate with horizon interactions? (F) Produce a final summary Summarize the entire evolution in 8–12 sentences. Here is the structure to follow: 1. Initial state (two objects) 2. First collapse 3. Second collapse 4. Horizon formation 5. Pre-merger interaction phase 6. Horizon merger event 7. Stabilization as one final object 8. Final energy + entropy state ________________________________________ 4. Important Instructions for the AI • Do not invent physics terms. Just analyze the patterns. • Use visual plots wherever possible. • Interpret everything strictly from the CSV values. • Provide clear, beginner-friendly explanations. • If something is ambiguous, state what the data suggests. ________________________________________ 5. Final Output Checklist Your final answer must include: ✔ A description of all plots ✔ Identification of collapse moments ✔ Identification of horizon formation ✔ The merger timestep ✔ Interpretation of entropy changes ✔ Interpretation of flux_out changes ✔ A clear narrative of the entire sequence

Rotating Black Hole + Galaxy Disk + Δτ-Slice Geometry Jets emerging from pure adjacency physics This simulation is the astrophysical-scale extension of UDEL — the same discrete substrate that powers the Full Genesis model — now applied to rotating black holes, galactic disks, multi-slice curvature, and jet formation. It reproduces, using nothing but the UDEL rules: • rotating accretion disks • non-singular black hole horizons • bulk gravity from Δτ-slices • galaxy-scale mass distribution • NFW-like halo formation • and finally fully collimated relativistic jets All from the same mathematical postulates used in Book 3 – Void Engineering, Chapters 4–10. No new forces. No magnetohydrodynamics. No relativity equations. Just adjacency, hop probabilities, Δt, Δτ, and path-density

Full technical specifications and detailed instructions are available in the downloadable PDF above.

You are a data analyst studying the output of a physics-based simulation. You do not need to know any astrophysics or domain theory. Everything required for interpretation is defined below. After reading this prompt, you must wait for me to upload the CSV. Do not guess any values. Do not summarize before the file is provided. If Python execution is available, use it to generate plots. ________________________________________ ==================================================== BACKGROUND — Read carefully ==================================================== This CSV contains time-series data from a discrete-lattice astrophysical simulation. Each row is one time-step. Each column is a measurement taken at that time. The columns fall into several groups: ________________________________________ 1. Basic columns • step • total_E_all_slices • E_visible These track global progress and visible-layer energy. ________________________________________ 2. Horizon diagnostics • num_horizon • BH_entropy • hor_r_perp_rms • hor_z_rms • hor_aspect Interpretation: • num_horizon → size of delay boundary • BH_entropy → boundary complexity • hor_aspect → shape o 1 = flattened o larger then 1 = vertically stretched ________________________________________ 3. Visible-layer energy distribution • min_E_vis, max_E_vis, mean_E_vis, median_E_vis • std_E_vis, entropy_vis, active_vis Guidelines: • entropy_vis↑ → energy spreads / becomes less ordered • active_vis → number of non-zero nodes ________________________________________ 4. Radial & shell geometry • r_mean, r_std • peak_shell, peak_energy • shell_0, shell_1, … Interpretation: • peak_shell → “radius” of dominant energy • peak_energy → how strong that radius is ________________________________________ 5. Jet diagnostics (critical) • jet_polar_E • jet_equator_E • jet_outer_E • jet_contrast Interpretation: • jet_polar_E → energy in polar cones • jet_equator_E → energy in equatorial band • jet_contrast = polar / equator Rules of thumb: • contrast smaller then 1 → no jets • 1–3 → mild asymmetry • 10 → strong jets • 30 → highly collimated jets ________________________________________ 6. Slice energies Columns like: • E_slice_alpha_-3 … E_slice_alpha_+3 These track the energy distribution across Δτ-slices; values should remain stable. ________________________________________ ==================================================== YOUR TASKS (When I upload the CSV) ==================================================== A) BASIC OVERVIEW 1. Summarize overall evolution: o Does energy spread or concentrate? o Does the boundary grow or stabilize? o Does the system settle or remain dynamic? 2. Identify any surprising or sudden transitions. ________________________________________ B) PLOT THE KEY QUANTITIES Generate clear, labeled plots for: 1. jet_polar_E vs step 2. jet_equator_E vs step 3. jet_contrast vs step 4. jet_outer_E vs step 5. num_horizon vs step 6. entropy_vis vs step 7. peak_shell vs step 8. BH_entropy vs step 9. r_mean vs step 10. E_slice_alpha_* (all slices, stacked or separate) ________________________________________ C) DETECT JET FORMATION Answer clearly: • Did jets form? • When did they appear? • How strong did they become? • Did jet_contrast stabilize or grow continuously? • How do jet_polar_E and jet_equator_E evolve? ________________________________________ D) ANALYZE HORIZON BEHAVIOR Explain: • growth or stabilization of num_horizon • changes in BH_entropy • how hor_aspect evolves (flat, round, elongated) ________________________________________ E) ENERGY GEOMETRY Interpret: • r_mean • peak_shell • shell distributions to explain: • outflow vs inflow • disk thickening or thinning • central concentration vs diffusion ________________________________________ F) FULL INTERPRETATION Provide a clear narrative of the system’s behavior over time, using only the measurements defined above. Avoid domain theory — focus on trends, shapes, and dynamics. ________________________________________ After you read this prompt, wait for the CSV. Do not compute anything until I upload the file.

The UDEL Double-Slit Simulator is a fully discrete, wave-free, singularity-free reconstruction of the famous double-slit experiment. It does not use wavefunctions, complex amplitudes, differential equations, or Schrödinger evolution. Instead, it uses the pure UDEL model: A universe is a discrete lattice of nodes. Energy hops from node to node based on adjacency-based transition probabilities T_ij. Interference patterns emerge purely from overlapping path ensembles, not waves. “Collapse” arises from path pruning, not non-unitary jumps. Detectors modify adjacency and prune paths → the interference disappears. This simulation visually and numerically reproduces: Double-slit interference Single-slit diffraction Interference destruction when a detector is introduced —all using only UDEL’s combinatorial rules. It is one of the cleanest demonstrations of how “quantum weirdness” dissolves once you replace waves with paths.

Full technical specifications and detailed instructions are available in the downloadable PDF above.

✅ LLM Analysis Prompt for UDEL Double-Slit Simulation Copy–paste this into any AI system before uploading your CSV ________________________________________ UDEL DOUBLE-SLIT — ANALYSIS PROMPT You are an analytical assistant. I will upload a CSV file generated by a double-slit style simulation. This simulation tracks how energy travels across a 2D lattice and measures the intensity arriving at a “screen” after passing through: • Two slits (both open) • One slit (single slit only) • Both slits, but with a detector present Your job is to analyze the CSV, generate visualizations, and provide explanations. The CSV has the following columns: x, both_slits, single_slit, with_detector 0, ... 1, ... ... Where: • x = horizontal position on the detection screen • both_slits = measured intensity when both slits are open • single_slit = measured intensity when only the left slit is open • with_detector = measured intensity when both slits are open but a detector prunes paths ________________________________________ YOUR TASKS 1. Load and inspect the CSV • Display the first few rows • Report the number of data points • Confirm all values are numeric and valid ________________________________________ 2. Plot the three intensity curves Create a line plot: • Blue → both slits (interference pattern expected) • Orange → single slit (diffraction envelope) • Green (dashed) → both slits + detector (fringes suppressed) Label: • X-axis = Screen position • Y-axis = Intensity • Title = Screen Intensities ________________________________________ 3. Normalize the curves and plot again Normalize each dataset so the area under each curve equals 1. This produces probability-like profiles: • Compare fringe visibility • Compare widths • Show how measurement changes the distribution ________________________________________ 4. Extract quantitative features For each dataset: • Total intensity (sum) • Center of mass of the distribution • Standard deviation (spread) • Peak intensity and peak location • Fringe contrast between maxima and minima (for the both-slits case) Include a short interpretation of each quantity. ________________________________________ 5. Compare the three cases Explain: • Why both slits show an interference-like “ripple pattern” • Why single slit is smoother • Why with detector removes fringes • Whether the results resemble classical double-slit experiments (This does not require domain knowledge of the UDEL model—use generic reasoning.) ________________________________________ 6. Provide visual explanation diagrams (optional) If possible, generate simplified ASCII or plotted diagrams showing: • Expected path overlap • Differences in spread • Suppression of interference by a detector ________________________________________ 7. Write a final summary Summarize in clear, accessible language: • What patterns emerged • What changed when the detector was added • How the results align with the intuition of path-based interference ________________________________________ RESPONSE REQUIREMENTS • Produce clean, readable plots • Use professional labels, legends, and colors • Provide mathematical + intuitive explanations • Be accurate even if values are small or distributions are sharp When you're ready, say: “Upload your CSV and I will begin the analysis.” ________________________________________ END OF PROMPT