{ "version": "1", "updated_at": "2026-04-10", "tests": [ { "id": "photochem-multigeom-active-space", "name": "Photochemical multi-geometry active space (inZOR-ND) — planned benchmark", "category": "Quantum Chemistry", "status": "planned", "published": true, "created_at": "2026-04-10", "updated_at": "2026-04-10", "description": "Design study: shared small CAS/SA-CASSCF across multiple geometries along photochemical coordinates (ethylene torsion, butadiene torsion, optional fulvene). Compare inZOR-ND discrete search vs AVAS, NOON/MP2, random (matched budget). Metrics: convergence rate, mean energy, gap regularity, n_eval, Jaccard vs reference geometry, quasi-degenerate candidate clusters. Protocol and scripts: zor_active_space/photochem_multigeom/ in ZOR repo. No RL/NN/backprop; same emergent paradigm as existing benchmarks.", "key_findings": [ "Infrastructure already exists: CASFitness(List[MolSpec]), run_butadiene_pec.py, run_ethylene_sa.py, run_pec_benchmark.py", "Delivered: EXPERIMENTAL_PLAN.md, benchmark_specs.json, aggregate_metrics.py, public HTML stub", "Next: extend angle grids near quasi-degenerate regions; uniform summary.json from each driver; fill results table" ], "metrics": { "Systems phase 1": "Ethylene, butadiene (+ optional fulvene)", "Baselines": "inZOR-ND, AVAS, NOON/MP2, random" }, "images": [], "links": [ { "label": "Study page (plan + metrics)", "url": "tests/photochem_multi_geom_active_space/index.html", "primary": true }, { "label": "QC gaps unified (context)", "url": "tests/qc_gaps_h2_ethylene_unified/index.html" } ] }, { "id": "zor-ethylene-3d-quasidegenerate-regions", "name": "Ethylene (3D) — Quasi-Degenerate Gap Structure via inZOR-ND", "category": "Quantum Chemistry", "status": "finished", "published": true, "created_at": "2026-04-03", "updated_at": "2026-04-09", "description": "Standalone empirical test: 3D internal coordinates (torsion, r_CC, z_lift), SA-CASSCF(2,2), three engine seeds vs matched-budget uniform control, pooled low-gap DBSCAN clusters. Figures are PNGs from logs/validation_regional_3seeds_20260403_155239. Reproducibility: byte-identical seed-42 probes + cluster-stability comparator (HIGH; same-trace only). Listed first on Research Tests; separate from the unified QC chapter.", "key_findings": [ "Multiple quasi-degenerate basins; recurrence across seeds with real per-seed heterogeneity (Top-K sector weights differ)", "inZOR-ND reaches gap < 10 meV much earlier than uniform sampling on the matched budget", "Identical seed + protocol → byte-identical probes (determinism); cluster HIGH compares identical traces only", "Context: “QC gaps, H₂ & ethylene (inZOR-ND): unified report”" ], "metrics": { "Run seeds": "42, 137, 2011", "Dimensionality": "3D", "Method": "SA-CASSCF(2,2)" }, "images": [ { "url": "tests/qc_gaps_h2_ethylene_unified/figures/regional_155239_scatter_torsion_rcc_gap.png", "caption": "Regional 3D validation: torsion vs r_CC (gap colour)" }, { "url": "tests/qc_gaps_h2_ethylene_unified/figures/regional_155239_torsion_low_gap_hist.png", "caption": "Low-gap torsion histogram (same batch)" }, { "url": "tests/qc_gaps_h2_ethylene_unified/figures/regional_155239_local_gap_grid.png", "caption": "Local 5×5 gap grid (same batch)" }, { "url": "tests/qc_gaps_h2_ethylene_unified/figures/regional_155239_local_gap_z_sweep.png", "caption": "z_lift gap sweep (same batch)" } ], "links": [ { "label": "Full report (tables + figures)", "url": "tests/qc_gaps_h2_ethylene_unified/ethylene_3d_zor.html", "primary": true }, { "label": "QC gaps unified chapter (context)", "url": "tests/qc_gaps_h2_ethylene_unified/index.html" } ] }, { "id": "qc-gaps-h2-ethylene-unified", "name": "QC gaps, H₂ & ethylene (inZOR-ND): unified report — motivation, methods, results", "category": "Quantum Chemistry", "status": "finished", "published": true, "created_at": "2026-04-02", "updated_at": "2026-04-09", "description": "Single long-form page for the gap-focused QC thread: why electronic gaps matter near quasi-degeneracy; a minimal H₂ SA-CASSCF(2,2) HOMO–LUMO scan (inZOR-ND discrete search vs NOON, cc-pVDZ) with JSON; the ethylene_quasidegen scene (SA-CASSCF(2,2) continuous exploration, multi-seed validation, score variants, 2D/3D); and how this connects to the inZOR-ND active-space benchmarks (N₂, Cr₂, eight-benchmark comparative including ethylene). Separates discrete-search benchmarks from continuous exploration logs.", "key_findings": [ "Motivation: gaps are cheap sentinels for active-space pathology before expensive PEC or large-CAS work", "H₂: inZOR-ND gap monotone over 7 bond lengths; NOON selection shows a ~18 eV spike at R=2.0 Å in the saved snapshot", "Ethylene (inZOR-ND continuous mode): evolutionary exploration with SA-CASSCF(2,2) signals; validation across seeds/controls/grids; legacy vs local_coherent vs dwell_coherent", "Ethylene 3D regional validation is a separate Research Tests entry (not linked here)", "Discrete-search comparative remains the citation target for CAS benchmarks; continuous mode explores the same chemical stress in an ALife setting" ], "metrics": { "H₂ scan points": "7 (0.7–2.5 Å)", "Basis (H₂)": "cc-pVDZ", "Ethylene scene": "ethylene_quasidegen", "SA-CASSCF": "(2,2) for H₂ table & ethylene probe" }, "images": [ { "url": "tests/qc_gaps_h2_ethylene_unified/figures/ethylene_s0s1_gap.png", "caption": "Ethylene S₀–S₁ gap (inZOR-ND export)" }, { "url": "tests/qc_gaps_h2_ethylene_unified/figures/vladimir_final_comparison.png", "caption": "Validation / variant comparison (inZOR-ND)" }, { "url": "tests/qc_gaps_h2_ethylene_unified/figures/vladimir_answer.png", "caption": "Metrics panel (inZOR-ND validation thread)" } ], "links": [ { "label": "Full unified report", "url": "tests/qc_gaps_h2_ethylene_unified/index.html", "primary": true }, { "label": "Raw H₂ JSON", "url": "tests/qc_gaps_h2_ethylene_unified/h2_stretch_results.json" }, { "label": "inZOR-ND comparative", "url": "tests/active_space_comparative/index.html" }, { "label": "N₂ study", "url": "tests/active_space_n2/index.html" }, { "label": "Cr₂ study", "url": "tests/active_space_cr2/index.html" } ] }, { "id": "active-space-comparative", "name": "inZOR-ND: Comprehensive Validation for Automatic CASSCF Active Space Selection — 8 Benchmarks", "category": "Quantum Chemistry", "status": "finished", "published": true, "created_at": "2026-03-19", "updated_at": "2026-03-22", "description": "Comprehensive validation of inZOR-ND for CASSCF/SA-CASSCF active space selection across 8 benchmarks on 6 molecular systems: N₂ PEC with 3 basis sets (6-31G, cc-pVDZ, cc-pVTZ), Cr₂ (transition metal, 3 geometries), butadiene (torsion PEC, 7 angles), formaldehyde (SA-CASSCF, 3 states), benzene (CAS(8,8), 5.7 billion candidates), and ethylene (SA-CASSCF, S0/S1, 4 torsion angles). inZOR-ND wins all 8 benchmarks (3.7–430 kcal/mol advantage). NOON-MP2 or AVAS fail on 4 out of 8 systems. Corrected NOON-MP2 uses diagonal 1-RDM method.", "key_findings": [ "8/8 benchmarks: inZOR-ND wins or is the only method that works", "NOON-MP2 or AVAS did not converge on 4/8 benchmarks (CH₂O, benzene, ethylene AVAS)", "N₂ PEC: consistent advantage across 3 basis sets (5.8–42.8 kcal/mol)", "Cr₂ (3 geometries): −20 kcal/mol advantage over corrected NOON-MP2", "Butadiene torsion (7 angles): −3.7 kcal/mol vs NOON", "CH₂O SA-CASSCF: corrected NOON and AVAS did not converge; inZOR-ND is only converging method", "Benzene CAS(8,8): corrected NOON and AVAS did not converge; 562 evals out of 5.7 billion", "Ethylene SA-CASSCF (S0/S1): −430 kcal/mol vs NOON; AVAS not applicable (truncation)", "Engine completely unmodified across all 8 benchmarks — no chemical knowledge required" ], "metrics": { "Benchmarks won": "8/8", "Largest search space": "5.7×10⁹ (benzene)", "Best advantage": "430 kcal/mol (ethylene SA-CASSCF)", "Molecular systems": "6 (N₂, Cr₂, butadiene, CH₂O, benzene, ethylene)", "Basis sets": "6-31G, cc-pVDZ, cc-pVTZ, STO-3G", "Problem types": "Ground state, multi-geometry PEC, SA-CASSCF", "Baselines did not converge": "4/8 benchmarks", "Engine modifications": "0", "Baseline methods": "NOON-MP2 (corrected diagonal 1-RDM), AVAS" }, "images": [ { "url": "tests/active_space_comparative/figures/fig5_pec_all_methods.png", "caption": "N₂ PEC: inZOR-ND vs NOON-MP2/AVAS shared active space comparison" }, { "url": "tests/active_space_comparative/figures/fig9_cr2_baselines.png", "caption": "Cr₂ per-geometry comparison: inZOR-ND vs NOON-MP2/AVAS" }, { "url": "tests/active_space_comparative/figures/fig10_butadiene_pec.png", "caption": "Butadiene torsion PEC across 7 angles" }, { "url": "tests/active_space_comparative/figures/fig13_summary_all_benchmarks.png", "caption": "Summary: inZOR-ND advantage across all benchmarks" } ], "links": [ { "label": "Full Report", "url": "tests/active_space_comparative/index.html", "primary": true }, { "label": "QC gaps: H₂ + ethylene (unified)", "url": "tests/qc_gaps_h2_ethylene_unified/index.html" }, { "label": "N₂ Study", "url": "tests/active_space_n2/index.html" }, { "label": "Cr₂ Study", "url": "tests/active_space_cr2/index.html" } ] }, { "id": "active-space-cr2", "name": "inZOR-ND: Active Space Selection for Cr₂ — 30 Million Candidates, 0.005% Coverage", "category": "Quantum Chemistry", "status": "finished", "published": true, "created_at": "2026-03-19", "updated_at": "2026-03-19", "description": "inZOR-ND tackles the chromium dimer Cr₂ — one of the most controversial molecules in quantum chemistry, with a disputed bond order and extreme multi-reference character. From a search space of C(36,8) = 30,260,340 possible CASSCF(8,8) active spaces (STO-3G basis, R=1.68 Å equilibrium), inZOR-ND evaluates only 1,572 (0.0052%) and discovers the optimal active space containing Cr 3d/4s orbitals. 10 degenerate optima found. The search recovers from near-extinction to full saturation.", "key_findings": [ "Search space: C(36,8) = 30,260,340 — brute-force completely infeasible", "inZOR-ND evaluates 1,572 spaces (0.0052%) — 99.995% savings", "Optimal: MOs [8,20,21,22,24,27,33,35] — Cr 3d/4s character", "E(CASSCF) = −2064.595 Eh vs E(HF) = −2064.036 Eh → ΔE = −0.559 Eh", "10 degenerate optima (identical fitness) — symmetry equivalence discovered", "Near-extinction at step 14 → full recovery to 40 active candidates", "Wall time: 51.7 min on 14 cores — inZOR-ND engine completely unmodified" ], "metrics": { "Search space C(36,8)": "30,260,340", "Evaluated": "1,572 (0.0052%)", "Savings vs brute-force": "99.995%", "Degenerate optima": "10", "E(CASSCF)": "−2064.595 Eh", "Correlation captured": "−0.559 Eh", "Wall time (14 cores)": "3,100s (51.7 min)", "Basis set": "STO-3G", "MO pool": "36" }, "images": [ { "url": "tests/active_space_cr2/figures/fig1_cr2_coverage_population.png", "caption": "Cache growth & population recovery from near-extinction" }, { "url": "tests/active_space_cr2/figures/fig2_cr2_top10_degeneracy.png", "caption": "Top 10 active spaces — all degenerate (identical fitness)" } ], "links": [ { "label": "Full Report", "url": "tests/active_space_cr2/index.html" } ] }, { "id": "active-space-n2", "name": "inZOR-ND: Bio-Adaptive Active Space Selection — N₂ Dissociation (6-31G, 3 Geometries)", "category": "Quantum Chemistry", "status": "finished", "published": true, "created_at": "2026-03-19", "updated_at": "2026-03-19", "description": "inZOR-ND discovers the optimal CASSCF(6,6) active space for N₂ dissociation across 3 geometries (R=1.1, 1.5, 2.0 Å) using a 6-31G basis, evaluating only 2.91% of the 18,564 possible active spaces (C(18,6)). Without any chemical priors, orbital symmetry labels, or occupation-number thresholds, the bio-adaptive search naturally discovers 10 degenerate symmetry-equivalent optima under N₂'s D∞h point group. Correlation energy captured: 0.148–0.464 Eh across the dissociation curve.", "key_findings": [ "Optimal CASSCF(6,6) found: MOs [2,4,5,7,11,15] — σ/π/π*/σ* manifold", "Only 541 of 18,564 spaces evaluated (2.91%) — 97.1% savings vs brute-force", "10 degenerate optima discovered — symmetry-equivalent under D∞h", "Convergent at all 3 geometries: E(CASSCF) = −109.016 / −108.886 / −108.773 Eh", "Correlation energy grows 3× at dissociation (0.148 → 0.464 Eh) — correct physics", "Healthy convergence — search saturated to optimal fitness", "inZOR-ND engine used completely unmodified" ], "metrics": { "Search space C(18,6)": "18,564", "Evaluated": "541 (2.91%)", "Savings vs brute-force": "97.1%", "Degenerate optima": "10", "Best E(CASSCF) R=1.1Å": "−109.016 Eh", "Correlation @ R=2.0Å": "−0.464 Eh", "Wall time (14 cores)": "1,244s (20.7 min)", "Basis set": "6-31G" }, "images": [ { "url": "tests/active_space_n2/figures/fig1_coverage_energy.png", "caption": "Search space coverage & CASSCF vs HF dissociation curve" }, { "url": "tests/active_space_n2/figures/fig2_degeneracy_organisms.png", "caption": "Fitness distribution (10 degenerate optima) & top organism genomes" }, { "url": "tests/active_space_n2/figures/fig3_population_evolution.png", "caption": "Population size & mean energy evolution over 120 steps" } ], "links": [ { "label": "Full Report", "url": "tests/active_space_n2/index.html" } ] }, { "id": "qec-zor-ibm", "name": "inZOR-ND: Hardware-Native Quantum Error Correction on IBM Quantum (7-qubit, Heron)", "category": "Quantum Computing", "status": "finished", "published": true, "created_at": "2026-03-16", "updated_at": "2026-03-16", "description": "inZOR-ND autonomously discovers 7-qubit QEC codes optimized for IBM Heron hardware, without any prior QEC knowledge. Two versions: v1 (21 Ry params, X-only correction) and v2 (42 Ry+Rz params, Full Pauli 22 syndromes, 6 test states, multi-noise training). Across 7 hardware runs on ibm_fez and ibm_marrakesh, inZOR-ND beats Steane [[7,1,3]] in every single run — from +0.2 pp to +5.1 pp. Hardware-native CZ circuits transpile to depth ~58 vs Steane's ~115 on Heron (~2x shallower). v1 LUPA achieves 0.999843 simulation fidelity; v2 reaches 0.990244 (multi-noise average).", "key_findings": [ "inZOR-ND beats Steane [[7,1,3]] in 7/7 IBM hardware runs (v1 + v2)", "v1: 21 Ry params, X-only correction, sim fidelity 0.999843 (LUPA)", "v2: 42 Ry+Rz params, Full Pauli (22 syndromes), 6 test states, multi-noise training", "Hardware-native ansatz: 12 CZ gates on IBM Fez tree topology, ~2x shallower than Steane", "Best gain (noisy regime): inZOR-ND 0.698 > Steane 0.647 (+5.1 pp)", "v2 Qiskit cross-validation: perfect match (diff < 1e-16)", "Validated on ibm_fez (3 runs) + ibm_marrakesh (4 runs), 8000 shots each" ], "metrics": { "v1 sim fidelity (LUPA)": "0.999843", "v2 sim fidelity (multi-noise)": "0.990244", "Best HW gain vs Steane": "+5.1 pp", "Hardware runs won": "7/7", "v1 parameters": "21 (Ry)", "v2 parameters": "42 (Ry+Rz)", "v2 syndromes": "22 (Full Pauli X+Y+Z)", "CZ gates (encoding)": "12", "Transpiled depth inZOR-ND": "~58", "Transpiled depth Steane": "~115", "Hardware": "IBM Heron (ibm_fez, ibm_marrakesh)", "Total hardware runs": "7" }, "images": [ { "url": "tests/qec_zor/figures/fig1_hardware_results.png", "caption": "All 7 hardware runs on IBM Quantum (v1 + v2). inZOR-ND beats Steane in every run." }, { "url": "tests/qec_zor/figures/fig7_v1_vs_v2.png", "caption": "v1 vs v2 head-to-head on ibm_marrakesh (same calibration day)." }, { "url": "tests/qec_zor/figures/fig2_circuit_depth.png", "caption": "Circuit depth: inZOR-ND v1/v2 transpile to ~53-58 vs Steane ~115 on IBM Heron." } ], "links": [ { "label": "Full Details", "url": "tests/qec_zor/index.html", "primary": true }, { "label": "PDF (Zenodo preprint)", "url": "zenodo_publish/qec_zor/inZORi_QEC_Zenodo.pdf" } ] }, { "id": "fusion-disruption-proximity", "name": "inZOR-ND: Machine-Agnostic Disruption Proximity Law from Plasma Current Dynamics", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-03-06", "updated_at": "2026-03-06", "description": "A compact empirical indicator η for disruption proximity in tokamak plasmas, derived solely from plasma current (Ip) dynamics using the inZOR-ND bio-adaptive discovery engine. The 3-term candidate law η ≈ −kurtosis + 0.80·rate_ratio + 0.65·cv_ratio encodes a universal structural nucleus (kurtosis) and two instability components. Validated cross-machine on MAST (674 D / 3647 N), C-Mod (414 D / 13 N), and HL-2A (296 D / 600 N) without machine-specific retraining.", "key_findings": [ "3-term law: η ≈ −kurtosis + 0.80·rate_ratio + 0.65·cv_ratio — derived from Ip only, no retraining", "min(sep) = 1.071 cross-machine (C-Mod is hardest: 414 D / 13 N, sep still > 1)", "Global ROC-AUC = 0.982, Global PR-AUC = 0.945", "LOMO PASS: all 3 folds (MAST, C-Mod, HL-2A each left out) — formula generalises", "Bootstrap CI₉₅ min(sep) = [0.373, 1.211] > 0 — robust to shot-level resampling", "Sensitivity ±20%: min(sep) ∈ [0.858, 1.254] — insensitive to coefficient variation", "η(t) rises systematically toward disruption on all 3 machines — disruption clock property", "Ablation confirms: 1-term < 2-term < 3-term in min(sep)" ], "metrics": { "Law candidate": "η ≈ −kurtosis + 0.80·rate_ratio + 0.65·cv_ratio", "min(sep)": "1.071", "Global ROC-AUC": "0.982", "Global PR-AUC": "0.945", "LOMO folds passed": "3/3", "Bootstrap CI 2.5% min(sep)": "0.373 (> 0)", "Sensitivity min(sep) range": "[0.858, 1.254]", "Tokamaks": "MAST, C-Mod, HL-2A", "Total disruptive windows": "1,384", "Total non-disruptive windows": "4,260", "Discovery engine": "inZOR-ND bio-adaptive genomic search" }, "images": [ { "url": "tests/fusion_disruption/figures/fig0_overview_discovery_path.png", "caption": "Fig 0 — Discovery path: min(sep) staircase from 1-term to 3-term. inZOR-ND navigates the law-space." }, { "url": "tests/fusion_disruption/figures/fig1_ablation_comparison.png", "caption": "Fig 1 — Baseline / Ablation: 1-term < 2-term < 3-term in cross-machine robustness." }, { "url": "tests/fusion_disruption/figures/fig2_lomo.png", "caption": "Fig 2 — LOMO: sep and ROC-AUC for each left-out machine. All 3 folds PASS." }, { "url": "tests/fusion_disruption/figures/fig3_temporal_eta.png", "caption": "Fig 3 — Temporal trajectory η(t): systematic rise toward disruption on all 3 machines." }, { "url": "tests/fusion_disruption/figures/fig4_structural_per_machine.png", "caption": "Fig 4 — Per-machine structural analysis: kurtosis common nucleus; rate_ratio dominant on MAST, cv_ratio on C-Mod." }, { "url": "tests/fusion_disruption/figures/fig5_robustness_sensitivity.png", "caption": "Fig 5 — Robustness (bootstrap 300 replicas) and sensitivity (±20% coefficients, 300 replicas)." } ], "links": [ { "label": "Full Details", "url": "tests/fusion_disruption/index.html", "primary": true }, { "url": "results/fusion_disruption_ablation.json", "label": "Ablation JSON" }, { "url": "results/fusion_disruption_lomo.json", "label": "LOMO JSON" }, { "url": "results/fusion_disruption_robustness.json", "label": "Robustness JSON" }, { "label": "PDF (Zenodo preprint)", "url": "tests/fusion_disruption/inZOR-ND_Fusion_Disruption_Proximity_Zenodo.pdf" }, { "label": "Zenodo (DOI)", "url": "https://doi.org/10.5281/zenodo.18916249" } ], "domain": "Plasma Physics / Fusion Disruption", "short_description": "inZOR-ND discovers a 3-term machine-agnostic disruption proximity law from Ip dynamics alone. Validated on MAST, C-Mod, HL-2A with LOMO, bootstrap, and sensitivity analysis.", "doi": "10.5281/zenodo.18916249", "zenodo_url": "https://doi.org/10.5281/zenodo.18916249" }, { "id": "baws-nr-universal", "name": "BAWS-NR: Universal 1.59× Speedup for Newton-Raphson Across 6 Scientific Domains", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-27", "updated_at": "2026-02-27", "description": "Bio-Adaptive Warm-Start Newton-Raphson (BAWS-NR) with α=0.979, validated across 6 independent domains: power systems (1354-bus PEGASE, 130K solves), celestial mechanics (Kepler), thermodynamics (Van der Waals), robotics (inverse kinematics), finance (Black-Scholes), and 2D nonlinear thermal conduction (900 unknowns, 10K solves). Includes formal convergence proof and no-harm guarantee. Total: 142,056 converged NR solves.", "key_findings": [ "Universal speedup: 1.59× mean across 6 domains (range 1.26×–2.13×)", "Convergence proof: BAWS-NR never worsens convergence when δ < D", "Dimensionality independent: works for 1D, 900D, and 1354D systems", "New 2D thermal conduction test: 10,000 solves, 1.73× speedup, all solutions identical", "Zero overhead: warm-start computation O(n), negligible vs J⁻¹F", "No domain-specific tuning: α=0.979 works out-of-the-box everywhere", "142,056 total converged NR solves — 100% convergence preservation" ], "metrics": { "Mean speedup": "1.59×", "Domains validated": "6", "Total NR solves": "142,056", "Alpha parameter": "0.979", "Convergence rate": "100%", "Thermal 2D speedup": "1.73×", "Power Flow speedup": "1.66×", "Kepler speedup": "2.13×" }, "images": [ { "url": "tests/baws_nr_study/figures/fig4_cross_domain.png", "caption": "Fig 1 — Cross-domain validation: α=0.979 provides consistent speedup across 6 independent scientific domains" }, { "url": "tests/baws_nr_study/figures/fig1_iterations_by_beta.png", "caption": "Fig 2 — 2D Thermal Conduction: iteration comparison by nonlinearity parameter β" }, { "url": "tests/baws_nr_study/figures/fig2_iteration_trace.png", "caption": "Fig 3 — Iteration trace: BAWS-NR consistently requires fewer iterations per timestep" }, { "url": "tests/baws_nr_study/figures/fig3_iteration_histogram.png", "caption": "Fig 4 — Iteration distribution: NR peaks at 5–6, BAWS-NR peaks at 3" } ], "links": [ { "label": "Full Details", "url": "tests/baws_nr_study/index.html", "primary": true }, { "url": "tests/baws_nr_study/index.html", "label": "Full Results Page" }, { "url": "results/thermal_baws_results.json", "label": "Raw JSON Results (Thermal 2D)" }, { "label": "Zenodo (DOI)", "url": "https://doi.org/10.5281/zenodo.18816838" } ], "doi": "10.5281/zenodo.18816838", "zenodo_url": "https://doi.org/10.5281/zenodo.18816838" }, { "id": "re-study-n1-security", "name": "inZORi: 1.66× Faster Real-Time N-1 Grid Security Under Renewable Volatility", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-27", "updated_at": "2026-02-27", "doi": "10.5281/zenodo.18807539", "zenodo_url": "https://doi.org/10.5281/zenodo.18807539", "description": "Quantifies how inZORi's bio-adaptive warm-start reduces Newton-Raphson iterations for N-1 contingency power flow convergence. 132,480 N-1 assessments on case1354pegase (1354 buses) using real ENTSO-E Germany Q2 2025 data (8,832 intervals). Identical tolerance (1e-6 MVA). inZORi converges in 3.15 iterations vs NR 5.22 — a 1.66× speedup translating to 1,058 vs 638 contingencies/min.", "key_findings": [ "Global speedup: 1.66× (inZORi 3.15 iter vs NR 5.22 iter, identical 1e-6 tolerance)", "Throughput: 1,058 vs 638 contingencies/min — +66% more N-1 security checks", "Peak speedup 1.99× at 0.92× load (normal operation)", "Both solvers solve exactly the same 130,056 cases; fail on same 2,424 (physical collapse >1.31×)", "Advantage consistent from 0.90× to 1.31× load — full physical operating range", "132,480 N-1 assessments: 8,832 ENTSO-E intervals × 5 lines × 3 seeds" ], "metrics": { "inZORi mean iterations": "3.151", "NR mean iterations": "5.221", "Iterations saved": "+2.07 per contingency", "Speedup": "1.66×", "inZORi ctg/min": "1,058", "NR ctg/min": "638", "N-1 assessments": "132,480", "Network": "case1354pegase (1354 buses, 1991 lines)", "Data": "ENTSO-E DE-LU Q2 2025, 8,832 intervals", "Seeds": "3 (42, 7, 137)" }, "images": [ { "url": "tests/re_study/figures/fig1_iterations_comparison.png", "caption": "Fig 1 — NR vs inZORi: minimum iterations to converge by load factor" }, { "url": "tests/re_study/figures/fig2_speedup_curve.png", "caption": "Fig 2 — Speedup curve across full operating range (0.90×–1.31×)" }, { "url": "tests/re_study/figures/fig3_contingencies_per_minute.png", "caption": "Fig 3 — Operational impact: N-1 contingencies verified per minute" }, { "url": "tests/re_study/figures/fig4_savings_and_failures.png", "caption": "Fig 4 — Iteration savings and failure behavior at physical limits" } ], "links": [ { "label": "Full Details", "url": "tests/re_study/index.html", "primary": true }, { "url": "tests/re_study/index.html", "label": "Full Results Page" }, { "url": "results/entsoe_iter_study.json", "label": "Raw JSON Results" }, { "url": "tests/re_study/inZORi_RE_Study_N1_Security_Zenodo.pdf", "label": "PDF (Zenodo preprint)" } ] }, { "id": "pfdelta-phase6-capacity", "name": "PFΔ Phase 6 ★ FINAL — Capacity Boundary on case1354pegase (1354-bus Pan-European)", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-25", "updated_at": "2026-02-27", "doi": "10.5281/zenodo.18806643", "zenodo_url": "https://doi.org/10.5281/zenodo.18806643", "description": "Final chapter of the inZORi research program. Discovers the precise critical load threshold on the case1354pegase Pan-European network (1354 buses, ~73 GW nominal). Systematic sweep from 1.18× to 1.30× nominal across 3 seeds. At exactly 1.25×, Newton-Raphson collapses to 0% S3 convergence on ALL 3 seeds while inZORi FrozenElite maintains 99.9% — a +99.9pp advantage. Includes full journey conclusions across all 6 phases.", "key_findings": [ "Sharp critical threshold at 1.25× nominal (~91.4 GW): NR=0%, inZORi=99.9% — reproducible on 3 seeds", "inZORi extends operable load range by ~5.5%: from 1.22× (both work) to 1.27× (inZORi only)", "Below 1.22×: all methods equivalent (~100%) — inZORi adds no overhead in normal conditions", "Above 1.30×: all methods fail — inZORi cannot solve physically infeasible systems", "11× larger network than Phases 1–5 (1354 vs 118 buses) — advantage holds at scale", "54 jobs completed in 290s on 12 cores — production-ready computational efficiency" ], "metrics": { "inZORi at 1.25× (critical)": "99.9% ±0.1", "NR at 1.25× (critical)": "0.0% ±0.0", "Advantage at critical point": "+99.9pp (∞× factor)", "Critical threshold": "1.25× nominal = ~91.4 GW", "Network": "case1354pegase (1354 buses, 1991 lines)", "Seeds": "3 (fully reproducible)", "Total jobs": "54 (6 scales × 3 variants × 3 seeds)", "Wall time": "290 seconds on 12 cores" }, "images": [ { "url": "tests/pfdelta_phase6_capacity/figures/fig1_capacity_curve.png", "caption": "Fig 1 — inZORi vs NR Capacity Curve: S3 convergence rate vs load scale (1.18×–1.30×). Sharp NR collapse at 1.25×." }, { "url": "tests/pfdelta_phase6_capacity/figures/fig2_reproducibility.png", "caption": "Fig 2 — Reproducibility at 1.25×: NR=0% on all 3 seeds, inZORi=99.8–100% on all 3 seeds." }, { "url": "tests/pfdelta_phase6_capacity/figures/fig3_heatmap.png", "caption": "Fig 3 — Full results heatmap: convergence across all 6 load levels × 3 variants. Sharp transition visible." }, { "url": "tests/pfdelta_phase6_capacity/figures/fig4_journey.png", "caption": "Fig 4 — Complete inZORi journey: all 6 phases from 118-bus synthetic to 1354-bus capacity boundary." } ], "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase6_capacity/index.html", "primary": true }, { "url": "tests/pfdelta_phase6_capacity/index.html", "label": "Full Results Page" }, { "url": "results/capacity_full_results.json", "label": "Raw JSON Results" } ] }, { "id": "pfdelta-phase5-entsoe", "name": "PFΔ Phase 5 — ENTSO-E Real Load: Romania, Germany, France (2024)", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-23", "updated_at": "2026-02-27", "doi": "10.5281/zenodo.18806567", "zenodo_url": "https://doi.org/10.5281/zenodo.18806567", "description": "First validation of inZORi FrozenElite on live ENTSO-E Transparency Platform data (2024). Three grids tested: Romania (thermal/hydro), Germany (renewables-heavy), France (nuclear-dominant). 4 variants × 4 seeds × 3 countries = 48 jobs on 12 cores. FrozenElite achieves 75–92% S3 convergence vs 4.5–15% for NR standard — a 6× to 17× improvement on real European consumption data.", "key_findings": [ "FrozenElite 75.3% vs NR 4.5% on Romania ENTSO-E 2024 (×16.7 advantage)", "FrozenElite 82.5% vs NR 11.4% on Germany ENTSO-E 2024 (×7.2 advantage)", "FrozenElite 91.7% vs NR 15.1% on France ENTSO-E 2024 (×6.1 advantage)", "Grid structure explains performance: nuclear-stable FR > renewables DE > thermal RO", "Adaptive selector ≈ best static genome (G01) on real data — G01 generalizes well", "Real ENTSO-E profiles less extreme than synthetic — validates under realistic conditions" ], "metrics": { "RO — FrozenElite S3": "75.3%", "RO — NR Standard S3": "4.5%", "DE — FrozenElite S3": "82.5%", "DE — NR Standard S3": "11.4%", "FR — FrozenElite S3": "91.7%", "FR — NR Standard S3": "15.1%", "Max advantage factor": "×16.7 (RO)", "Total jobs": "48 (3 countries × 4 variants × 4 seeds)", "Data source": "ENTSO-E API 2024 — 8,760 h/country" }, "images": [ { "url": "tests/pfdelta_phase5_entsoe/figures/fig1_s3_convergence.png", "caption": "Fig 1 — S3 Convergence Rate: FrozenElite vs G01 vs NR vs Synthetic across RO/DE/FR (ENTSO-E 2024)" }, { "url": "tests/pfdelta_phase5_entsoe/figures/fig2_fe_advantage.png", "caption": "Fig 2 — Absolute advantage (pp) and multiplicative factor: FrozenElite vs NR on real ENTSO-E data" }, { "url": "tests/pfdelta_phase5_entsoe/figures/fig3_recovery_times.png", "caption": "Fig 3 — Average recovery steps after N-1/N-2/N-4 contingencies: FrozenElite recovers 2–6× faster" }, { "url": "tests/pfdelta_phase5_entsoe/figures/fig4_phase_progression.png", "caption": "Fig 4 — Research progression Phase 1→5: advantage gap widens as test conditions become more realistic" } ], "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase5_entsoe/index.html", "primary": true }, { "url": "tests/pfdelta_phase5_entsoe/index.html", "label": "📄 Full Report (HTML)" }, { "url": "results/real_vs_baseline.json", "label": "📊 Raw Results (JSON)" }, { "url": "zenodo_publish/phase5/inZORi_PFDelta_Phase5_Zenodo.pdf", "label": "📥 PDF Zenodo" } ] }, { "id": "pfdelta-phase4-real", "name": "PFΔ Phase 4 — Real Load Profiles UA/DE/FR + Historical Blackouts", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-21", "updated_at": "2026-02-21", "description": "inZORi FrozenElite validated on real OPSD load profiles (Ukraine, Germany, France) and three historical blackout events: Balkans 2006, Ukraine Cyberattack 2015, Moldova 31 Jan 2026. 5 seeds × 4 variants × 3 countries. CI95 non-overlapping advantage confirmed.", "key_findings": [ "FrozenElite +3.64% over NR on Ukraine real profile (CI95 non-overlapping)", "FrozenElite +3.69% over NR on Germany real profile", "FrozenElite +3.31% over NR on France real profile", "Balkans 2006 (N-2): 90.7% convergence vs 88.8% NR during event", "Moldova 31 Jan 2026 (N-1 Isaccea-Vulcanesti): 22.5% vs 21.4% NR", "Advantage consistent across post-Soviet, renewables-heavy, and nuclear-dominant grids" ], "metrics": { "countries": [ "UA", "DE", "FR" ], "seeds": 5, "steps": 50000, "variants": 4, "total_runs": 60, "ua_advantage": "+3.64%", "de_advantage": "+3.69%", "fr_advantage": "+3.31%", "blackout_scenarios": 3, "runtime_min": 65.2 }, "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase4_real/index.html", "primary": true }, { "label": "Full Details", "url": "tests/pfdelta_phase4_real/" } ], "images": [ { "filename": "fig1_s3_convergence_countries.png", "caption": "S3 convergence per country with CI95", "url": "tests/pfdelta_phase4_real/figures/fig1_s3_convergence_countries.png" }, { "filename": "fig3_blackout_scenarios.png", "caption": "Blackout scenario replay results", "url": "tests/pfdelta_phase4_real/figures/fig3_blackout_scenarios.png" } ], "domain": "Power Systems / Real Data Validation", "short_description": "inZORi FrozenElite on real European load profiles (UA/DE/FR) + 3 historical blackouts. +3.3–3.7% advantage over NR, CI95 confirmed.", "doi": "10.5281/zenodo.18735099" }, { "id": "pfdelta-phase3-n2", "name": "PFΔ Phase 3 — N-2 Contingency Robustness (IEEE 118-bus)", "category": "scientific", "status": "finished", "published": true, "created_at": "2026-02-21", "updated_at": "2026-02-21", "description": "inZORi Frozen Elite with N-2 specialists: simultaneous outage of 2 lines. +16.14 pp vs Baseline A, 2.8× faster N-2 recovery. LUPA-refined genome (85.20%). 20 seeds × 50k steps, N-1+N-2 in S3.", "key_findings": [ "inZORi N-2: 71.16% S3 conv. (CI95 [70.9, 71.4])", "Baseline A: 55.02% | Baseline B: 55.35%", "N-2 recovery: 1.45 steps (inZORi) vs 4.12 steps (Baseline A) — 2.8× faster", "N-1 recovery ≈ N-2 recovery (1.49 vs 1.45 steps) — specialists neutralize N-2", "LUPA refined genome: 0.8444 (evo) → 0.8520 (+0.76pp)", "15-gen evolution + LUPA + 8-genome Frozen Elite pool" ], "metrics": { "s3_convergence_inzori": "71.16%", "s3_convergence_baseline_a": "55.02%", "improvement_pp": "+16.14", "n2_recovery_inzori": "1.45 steps", "n2_recovery_baseline": "4.12 steps", "recovery_advantage": "2.8x", "seeds": 20, "steps": 50000, "genome_fitness_lupa": "85.20%" }, "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase3_n2/index.html", "primary": true }, { "label": "Full Details", "url": "tests/pfdelta_phase3_n2/" } ], "images": [ { "filename": "fig1_s3_convergence_n2.png", "caption": "S3 convergence N-1+N-2", "url": "tests/pfdelta_phase3_n2/figures/fig1_s3_convergence_n2.png" }, { "filename": "fig2_recovery_n1_n2.png", "caption": "N-1 vs N-2 recovery speed", "url": "tests/pfdelta_phase3_n2/figures/fig2_recovery_n1_n2.png" } ], "domain": "Power Systems / Grid Stability", "short_description": "N-2 contingency (2 simultaneous line outages): inZORi +16pp, 2.8× faster recovery, LUPA-refined genome.", "doi": "10.5281/zenodo.18735120" }, { "id": "scenario-1-dynamic-worlds", "name": "Scenario 1: Adaptation to dynamic environment", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-14", "description": "Population of 40 organisms adapts to periodically alternating 2D environments (World A ↔ World B). Energy-conservation strategies emerge without explicit training.", "key_findings": [ "World A: Food bottom-left, danger top-right", "World B: Food top-right, danger bottom-left", "Population survives 4 world transitions over 600 steps", "Survival rate: ~99%", "Energy stabilizes through emergent conservation strategies" ], "metrics": { "steps": 600, "transitions": 4, "survival_rate": "99%", "initial_population": 40, "final_population": "~200" }, "links": [ { "label": "Full Details", "url": "tests/scenario1_dynamic/index.html", "primary": true } ], "images": [ { "filename": "scenario1_population_evolution_v2.png", "caption": "Population evolution in alternating worlds (A ↔ B) showing survival and adaptation over 600 steps", "url": "demo_images/scenario1_population_evolution_v2.png" } ], "domain": "Artificial life", "short_description": "Population of 40 organisms adapts to periodically alternating 2D environments (World A ↔ World B)", "created_at": "2026-02-10" }, { "id": "scenario-2-social-iq", "name": "Scenario 2: Emergence of social behavior (IQ & Fertility)", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-14", "description": "Population with IQ-encoded genomes. Higher-IQ individuals start reproduction later (education delay) and incur higher opportunity cost per child. No explicit fertility rules.", "key_findings": [ "Top 20 high-IQ pairs: 57 children", "Bottom 20 low-IQ pairs: 69 children", "Difference: -17.4%", "Effect strongest at extremes (~16-18% gap)", "Small biases (timing + opportunity cost) accumulate over time" ], "metrics": { "total_births": 122731, "top20_avg_iq": 134.0, "bottom20_avg_iq": 91.5, "top20_births": 57, "bottom20_births": 69, "difference_pct": -17.4 }, "links": [ { "label": "Full Details", "url": "tests/scenario2_social_iq/index.html", "primary": true } ], "images": [ { "filename": "scenario2_children_count_v2.png", "caption": "Children count comparison: high IQ vs low IQ pairs showing -17.4% difference due to delayed reproduction", "url": "demo_images/scenario2_children_count_v2.png" } ], "domain": "Social dynamics", "short_description": "Population with IQ-encoded genomes", "created_at": "2026-02-11" }, { "id": "scenario-4-tess", "name": "Scenario 3: inZORi prioritization for TESS", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-14", "description": "Multi-criteria prioritization of TESS light-curve signals using the same inZORi engine. Ranking is built from regularity, stability, SNR, scatter, transit count, duration and depth, without explicit manual weighting formulas.", "links": [ { "label": "Full Details", "url": "tests/scenario3_tess/index.html", "primary": true }, { "label": "Class distribution", "url": "demo_images/scenario4_tess_class_distribution.png" }, { "label": "Top-10 rank chart", "url": "demo_images/scenario4_tess_top10_ranksum.png" }, { "label": "Candidates report JSON", "url": "tests/tess/candidates_report.json" }, { "label": "Candidates report MD", "url": "tests/tess/candidates_report.md" } ], "key_findings": [ "13,000 signals analyzed in this published snapshot", "294 clean transit candidates flagged for follow-up", "Top candidate consensus rank_sum = 7 across 4 ranking variants", "Strong separation between clean transit vs variable/artifact classes", "Output includes ranked lists and anomaly classes for downstream review" ], "metrics": { "signals_analyzed": 13000, "clean_transit_candidates": 294, "uncertain": 12191, "variable_or_artifact": 513, "eclipsing_binary": 1, "weak_signal": 1, "ranking_variants": 4, "best_rank_sum": 7 }, "images": [ { "filename": "scenario4_tess_class_distribution.png", "caption": "Class distribution for prioritized TESS signals.", "url": "demo_images/scenario4_tess_class_distribution.png" }, { "filename": "scenario4_tess_top10_ranksum.png", "caption": "Top-10 consensus candidates ranked by rank_sum.", "url": "demo_images/scenario4_tess_top10_ranksum.png" } ], "domain": "Astronomy (TESS)", "short_description": "Multi-criteria prioritization of TESS light-curve signals using the same inZORi engine", "created_at": "2026-02-12" }, { "id": "scenario-5-robotics", "name": "Scenario 4: Adaptive robotics - navigation in changing environment", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-14", "description": "Robot navigates 100×100 grid for 800 steps across 5 phases with mobile hazards, relocating resources, and variable sensor noise. No map, no global reward—only survival pressure.", "key_findings": [ "Mean fitness (harsh_dynamic): 137.53", "Best seed (702): 139.27", "Max population: 1200", "Adaptation score: 0.744 (balanced across phases)", "Survival stabilizes around 550-560 steps", "Phase 3 shows maximal pressure from hazard barriers" ], "metrics": { "grid_size": "100×100", "total_steps": 800, "phases": 5, "mean_fitness": 137.53, "best_seed": 702, "best_fitness": 139.27, "max_population": 1200, "adaptation_score": 0.744 }, "images": [ { "filename": "best_trajectory.png", "caption": "Phase-colored trajectory showing robot's path on grid", "url": "demo_images/best_trajectory.png" }, { "filename": "fitness_evolution.png", "caption": "Fitness across successive evaluations with survival duration", "url": "demo_images/fitness_evolution.png" }, { "filename": "phase_survival.png", "caption": "Average survival steps by phase with adaptation score distribution", "url": "demo_images/phase_survival.png" }, { "filename": "hazard_resource_balance.png", "caption": "Hazard hits vs resources collected; greener = higher fitness", "url": "demo_images/hazard_resource_balance.png" } ], "links": [ { "label": "Full Details", "url": "tests/scenario4_robotics/index.html", "primary": true }, { "label": "Trajectory", "url": "demo_images/best_trajectory.png" }, { "label": "Fitness", "url": "demo_images/fitness_evolution.png" }, { "label": "Survival", "url": "demo_images/phase_survival.png" }, { "label": "Balance", "url": "demo_images/hazard_resource_balance.png" } ], "domain": "Robotics", "short_description": "Robot navigates 100×100 grid for 800 steps across 5 phases with mobile hazards, relocating resources, and variable sensor noise", "created_at": "2026-02-13" }, { "id": "scenario-6-sdc", "name": "Scenario 5: SDC_v1 - What inZORi preserves when world drifts slowly", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-14", "description": "Test of behavioral continuity under slow world drift vs collapse under chaotic drift. 2D environment with drifting food/danger zones (Gaussian fields). No optimization, no RL, no policy updates.", "key_findings": [ "Continuity (static): 0.98", "Continuity (drift_slow): 0.97", "Continuity (drift_medium): 0.73", "Continuity (drift_chaotic): 0.59", "inZORi preserves behavior when change is predictable", "Graceful degradation under unpredictable drift" ], "metrics": { "continuity_static": 0.98, "continuity_drift_slow": 0.97, "continuity_drift_medium": 0.73, "continuity_drift_chaotic": 0.59 }, "images": [ { "filename": "continuity_score_over_time.png", "caption": "Continuity remains high under static/slow drift, drops under medium, collapses under chaotic", "url": "demo_images/continuity_score_over_time.png" }, { "filename": "drift_tracking_distance.png", "caption": "How well population tracks moving food/danger zones", "url": "demo_images/drift_tracking_distance.png" }, { "filename": "tradition_reappearance_timeline.png", "caption": "When behavioral signatures reappear over time under drift", "url": "demo_images/tradition_reappearance_timeline.png" }, { "filename": "static_vs_chaotic.png", "caption": "Contrast between stable behavior and collapse", "url": "demo_images/static_vs_chaotic.png" } ], "links": [ { "label": "Full Details", "url": "tests/scenario5_sdc/index.html", "primary": true }, { "label": "Continuity", "url": "demo_images/continuity_score_over_time.png" }, { "label": "Tracking", "url": "demo_images/drift_tracking_distance.png" }, { "label": "Tradition", "url": "demo_images/tradition_reappearance_timeline.png" }, { "label": "Comparison", "url": "demo_images/static_vs_chaotic.png" } ], "domain": "Artificial life", "short_description": "Test of behavioral continuity under slow world drift vs collapse under chaotic drift", "created_at": "2026-02-13" }, { "id": "social-density-effect", "name": "inZORi Social Density Effect", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-15", "description": "Simulation of emergent intelligence in a 2D world with autonomous organisms under varying population densities (low: 20, medium: 60, high: 140 initial organisms). Measures collective behaviors, spatial distribution, mortality, energy stability, and identifies critical density thresholds. 24 seeds per density, 800 steps.", "key_findings": [ "Mortality rate increases significantly with density (LOW: 0.18%, MEDIUM: 0.24%, HIGH: 0.34%)", "Mean distance increases: LOW: 37.6 → HIGH: 42.8 (organisms spread out more at high density)", "Clustering decreases: LOW: 3.71 → HIGH: 2.86 (less spatial cohesion at high density)", "Genome diversity decreases slightly: LOW: 0.222 → HIGH: 0.211", "Energy mean remains stable across densities (~4.0-4.4)", "Statistical significance: mortality (p<0.0001), distance (p=0.0002), clustering (p=0.003), diversity (p<0.0001)", "Critical threshold observed between medium and high density regimes" ], "metrics": { "seeds": 24, "steps_per_run": 800, "densities": { "low": { "population_initial": 20, "population_final_mean": 386.5, "mortality_rate_mean": 0.001811, "mortality_rate_ci": "[0.00173, 0.00189]", "mean_distance": 37.63, "mean_distance_ci": "[35.54, 39.73]", "clustering": 3.71, "clustering_ci": "[3.28, 4.13]", "genome_diversity": 0.222, "genome_diversity_ci": "[0.219, 0.224]", "energy_mean": 3.95 }, "medium": { "population_initial": 60, "population_final_mean": 387.3, "mortality_rate_mean": 0.002368, "mortality_rate_ci": "[0.00227, 0.00246]", "mean_distance": 42.15, "mean_distance_ci": "[40.28, 44.01]", "clustering": 3.11, "clustering_ci": "[2.76, 3.45]", "genome_diversity": 0.221, "genome_diversity_ci": "[0.219, 0.222]", "energy_mean": 4.08 }, "high": { "population_initial": 140, "population_final_mean": 391.7, "mortality_rate_mean": 0.003379, "mortality_rate_ci": "[0.00325, 0.00351]", "mean_distance": 42.8, "mean_distance_ci": "[41.10, 44.50]", "clustering": 2.86, "clustering_ci": "[2.51, 3.21]", "genome_diversity": 0.211, "genome_diversity_ci": "[0.210, 0.212]", "energy_mean": 4.39 } }, "statistical_tests": { "mortality_low_vs_high": { "t_statistic": -20.5, "df": 38.16, "p_value": "< 0.0001" }, "distance_low_vs_high": { "t_statistic": -3.76, "df": 44.11, "p_value": 0.000172 }, "clustering_low_vs_high": { "t_statistic": 3.02, "df": 44.28, "p_value": 0.002522 }, "diversity_low_vs_high": { "t_statistic": 7.37, "df": 35.49, "p_value": "< 0.0001" } } }, "images": [ { "filename": "metrics_by_density.png", "caption": "Aggregated metrics comparison across low/medium/high density regimes", "url": "tests/social_density/metrics_by_density.png" }, { "filename": "population_series_low.png", "caption": "Population dynamics over 800 steps for LOW density (initial: 20)", "url": "tests/social_density/population_series_low.png" }, { "filename": "population_series_medium.png", "caption": "Population dynamics over 800 steps for MEDIUM density (initial: 60)", "url": "tests/social_density/population_series_medium.png" }, { "filename": "population_series_high.png", "caption": "Population dynamics over 800 steps for HIGH density (initial: 140)", "url": "tests/social_density/population_series_high.png" } ], "animations": [ { "filename": "density_low.gif", "caption": "Real-time visualization: LOW density regime (20 initial organisms)", "url": "tests/social_density/density_low.gif" }, { "filename": "density_medium.gif", "caption": "Real-time visualization: MEDIUM density regime (60 initial organisms)", "url": "tests/social_density/density_medium.gif" }, { "filename": "density_high.gif", "caption": "Real-time visualization: HIGH density regime (140 initial organisms)", "url": "tests/social_density/density_high.gif" } ], "links": [ { "label": "Full Details", "url": "tests/social_density_effect/index.html", "primary": true }, { "label": "Summary JSON", "url": "tests/social_density/summary.json" }, { "label": "Metrics plot", "url": "tests/social_density/metrics_by_density.png" }, { "label": "Population LOW", "url": "tests/social_density/population_series_low.png" }, { "label": "Population MEDIUM", "url": "tests/social_density/population_series_medium.png" }, { "label": "Population HIGH", "url": "tests/social_density/population_series_high.png" }, { "label": "GIF LOW", "url": "tests/social_density/density_low.gif" }, { "label": "GIF MEDIUM", "url": "tests/social_density/density_medium.gif" }, { "label": "GIF HIGH", "url": "tests/social_density/density_high.gif" } ], "domain": "Social dynamics", "short_description": "Simulation of emergent intelligence in a 2D world with autonomous organisms under varying population densities (low: 20, medium: 60, high: 140 initial organi…", "created_at": "2026-02-14" }, { "id": "zor-refract-20seeds", "name": "inZORi-REFRACT (20 seeds)", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-15", "description": "Emergent minimum-time/refraction behavior in a 2D Fast/Slow medium, run with real inZORi pipeline on 20 seeds.", "key_findings": [ "Strong angle separation control vs refract (p≈0.0).", "Cost efficiency improves under strong contrast (refract_vs_strong p<0.001).", "High target reach rates remain stable across conditions (~0.94–0.95).", "Natural local energetic sensing only; no explicit Snell law or RL.", "20-seed run improves robustness over pilot." ], "metrics": { "seeds": 20, "latest_eval": 10, "pass_flags": { "angles_diff_vs_control": true, "cheaper_than_direct": true, "effect_grows_with_contrast": false, "seed_robustness": true, "pass_all": false }, "condition_means": { "control_uniform": { "hit_rate": 0.9414, "time_to_target": 542.51, "cost_vs_direct": 0.0271, "angle_out_deg": 25.727 }, "refract": { "hit_rate": 0.9507, "time_to_target": 520.64, "cost_vs_direct": 0.0298, "angle_out_deg": 10.681 }, "contrast_strong": { "hit_rate": 0.9455, "time_to_target": 523.52, "cost_vs_direct": 0.0199, "angle_out_deg": 10.73 } } }, "images": [ { "filename": "angle_in_vs_out.png", "caption": "Incident vs refracted crossing angles (scatter + trend line).", "url": "tests/refract/angle_in_vs_out.png" }, { "filename": "cost_path_ratio_by_condition.png", "caption": "Cost-integrated path / geometric length by condition.", "url": "tests/refract/cost_path_ratio_by_condition.png" }, { "filename": "time_to_target_by_condition.png", "caption": "Time-to-target comparison across conditions.", "url": "tests/refract/time_to_target_by_condition.png" }, { "filename": "cost_vs_direct_ratio.png", "caption": "Cost to target versus direct baseline.", "url": "tests/refract/cost_vs_direct_ratio.png" } ], "animations": [ { "filename": "refract_control_uniform.gif", "caption": "Control uniform medium (baseline).", "url": "tests/refract/refract_control_uniform.gif" }, { "filename": "refract_refract.gif", "caption": "Refract condition (Fast/Slow contrast).", "url": "tests/refract/refract_refract.gif" }, { "filename": "refract_contrast_strong.gif", "caption": "Strong contrast condition.", "url": "tests/refract/refract_contrast_strong.gif" } ], "links": [ { "label": "Full Details", "url": "tests/refract_20seeds/index.html", "primary": true }, { "label": "Summary JSON", "url": "tests/refract/summary.json" }, { "label": "Angle plot", "url": "tests/refract/angle_in_vs_out.png" }, { "label": "Cost/path ratio", "url": "tests/refract/cost_path_ratio_by_condition.png" }, { "label": "Time-to-target", "url": "tests/refract/time_to_target_by_condition.png" }, { "label": "Cost vs direct", "url": "tests/refract/cost_vs_direct_ratio.png" }, { "label": "GIF control", "url": "tests/refract/refract_control_uniform.gif" }, { "label": "GIF refract", "url": "tests/refract/refract_refract.gif" }, { "label": "GIF strong", "url": "tests/refract/refract_contrast_strong.gif" } ], "domain": "Physics (optics)", "short_description": "Emergent minimum-time/refraction behavior in a 2D Fast/Slow medium, run with real inZORi pipeline on 20 seeds.", "created_at": "2026-02-15" }, { "id": "pfdelta-phase2-real", "name": "PFΔ Phase 2 — Real AC Power Flow (118-bus + N-1)", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-20", "description": "Full real AC Power Flow (pandapower) on IEEE 118-bus at 1.75× load (near-collapse). inZORi Frozen Elite achieves +16.8 pp convergence vs baselines and ~4× faster N-1 recovery. 20 seeds × 50k steps. Follows Phase 1 (surrogate), replacing it with real grid physics.", "key_findings": [ "inZORi Frozen Elite S3 conv.: 69.63% (CI95 [69.33, 69.92])", "Baseline A: 52.08% | Baseline B: 52.84%", "Improvement vs Baseline B: +16.8 pp (CI95 non-overlap ✓)", "N-1 recovery: 1.64 steps (inZORi) vs 6.80 steps (Baseline A) — ~4× faster", "Genome selection: O(1) cost, no online evolution at inference", "Consistent advantage across all 20 seeds" ], "metrics": { "network": "IEEE 118-bus (pandapower)", "load_scale": "1.75× nominal", "nr_max": 5, "n1_interval_s3": "300 steps", "seeds": 20, "steps": 50000, "s3_conv_inzori": "69.63%", "s3_conv_baseline_a": "52.08%", "s3_conv_baseline_b": "52.84%", "improvement_vs_b": "+16.8 pp", "recovery_inzori": "1.64 steps", "recovery_baseline": "~6.7 steps" }, "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase2_real/index.html", "primary": true }, { "label": "Zenodo (DOI)", "url": "https://doi.org/10.5281/zenodo.18717007" }, { "label": "Full Details", "url": "tests/pfdelta_phase2_real/index.html" }, { "label": "Phase 1 (Surrogate) →", "url": "tests/pfdelta_phase1_118/index.html" }, { "label": "S3 Convergence Chart", "url": "tests/pfdelta_phase2_real/figures/fig1_s3_convergence_comparison.png" }, { "label": "N-1 Recovery Chart", "url": "tests/pfdelta_phase2_real/figures/fig2_recovery_time_n1.png" }, { "label": "Phase 1 on Zenodo", "url": "https://doi.org/10.5281/zenodo.18716837" } ], "domain": "Power systems (Real AC PF)", "short_description": "Full real AC Power Flow on IEEE 118-bus at near-collapse load (1.75×). inZORi Frozen Elite vs static baselines under N-1 shocks.", "images": [ { "filename": "fig1_s3_convergence_comparison.png", "url": "tests/pfdelta_phase2_real/figures/fig1_s3_convergence_comparison.png", "caption": "S3 convergence rate comparison with CI95 error bars: inZORi 69.6% vs Baseline ~52%. +16.8 pp advantage confirmed." }, { "filename": "fig2_recovery_time_n1.png", "url": "tests/pfdelta_phase2_real/figures/fig2_recovery_time_n1.png", "caption": "Steps to first convergence after N-1 line restoration. inZORi: 1.64 steps vs ~6.7 baseline (~4× faster)." }, { "filename": "fig3_full_dashboard.png", "url": "tests/pfdelta_phase2_real/figures/fig3_full_dashboard.png", "caption": "Three-metric dashboard: S3 convergence, NR iterations, recovery steps." }, { "filename": "fig4_phase1_vs_phase2_evolution.png", "url": "tests/pfdelta_phase2_real/figures/fig4_phase1_vs_phase2_evolution.png", "caption": "Research evolution from Phase 1 surrogate to Phase 2 real AC PF." } ], "created_at": "2026-02-20", "doi": "10.5281/zenodo.18717007", "zenodo_url": "https://doi.org/10.5281/zenodo.18717007" }, { "id": "pfdelta-phase1-118", "name": "PFΔ Phase 1 (118 + N-1)", "category": "scientific", "status": "finished", "published": true, "updated_at": "2026-02-20", "description": "Streaming power flow robustness under multi-zone shocks and N-1 topology events. inZORi achieves 99.1% S3 convergence vs Baseline B 83.9% under severe_plus stress. Frozen-elites: 100% S3 conv with K=1. Topology recovery: inZORi 1.82 steps vs Baseline B 12.91 steps (7× faster). Phase 1 scope: 118-bus nonlinear surrogate. See Phase 2 for real AC PF results.", "key_findings": [ "inZORi S3 convergence: 99.1% (CI95 [98.9, 99.3]) vs Baseline B 83.9%", "Frozen-Top16-K1: 100% S3 convergence, 443.7 converged steps/sec", "Topology recovery: inZORi 1.82 steps vs Baseline B 12.91 steps (7× faster)", "World-level PF architecture: 40-50× speedup", "Fairness A/B validation, 30 seeds FULL, 20 seeds Frozen-Elites" ], "metrics": { "network": "118-bus", "seeds_full": 30, "s3_conv_zor": "99.1%", "s3_conv_baseline_b": "83.9%", "s3_conv_frozen": "100.0%", "topology_recovery_zor": "1.82 steps", "conv_steps_per_sec": "443.7" }, "links": [ { "label": "Full Details", "url": "tests/pfdelta_phase1_118/index.html", "primary": true }, { "label": "Zenodo (DOI)", "url": "https://doi.org/10.5281/zenodo.18716837" }, { "label": "Full Details", "url": "tests/pfdelta_phase1_118/index.html" }, { "label": "Core comparison chart", "url": "tests/pfdelta_phase1_118/figures/core_comparison_baselineA_baselineB_inZORi_frozen.png" }, { "label": "Phase 2 (Real AC PF) →", "url": "tests/pfdelta_phase2_real/index.html" }, { "label": "Phase 2 on Zenodo →", "url": "https://doi.org/10.5281/zenodo.18717007" } ], "domain": "Power systems (PFΔ)", "short_description": "Streaming power flow robustness under multi-zone shocks and N-1 topology events", "images": [ { "filename": "core_comparison_baselineA_baselineB_inZORi_frozen.png", "url": "tests/pfdelta_phase1_118/figures/core_comparison_baselineA_baselineB_inZORi_frozen.png", "caption": "Baseline A vs Baseline B vs inZORi vs Frozen-Top16-K1 from published real metrics (Table A1/A2)." }, { "filename": "convergence_over_time.png", "url": "tests/pfdelta_phase1_118/figures/convergence_over_time.png", "caption": "Convergence over time for the selected runtime strategy (stable at 1.0 across steps)." }, { "filename": "iterations_over_time.png", "url": "tests/pfdelta_phase1_118/figures/iterations_over_time.png", "caption": "NR iterations over time (compute usage and near-budget behavior)." }, { "filename": "pbl_over_time.png", "url": "tests/pfdelta_phase1_118/figures/pbl_over_time.png", "caption": "Power Balance Limit (PBL) over time (mean/p95 dynamics)." }, { "filename": "shock_events_timeline.png", "url": "tests/pfdelta_phase1_118/figures/shock_events_timeline.png", "caption": "Shock/N-1 event timeline (stress schedule and recovery windows)." }, { "filename": "fitness_progression_across_evals.png", "url": "tests/pfdelta_phase1_118/figures/fitness_progression_across_evals.png", "caption": "Fitness progression across evaluations (mean ± CI95)." }, { "filename": "seasonal_convergence_by_eval.png", "url": "tests/pfdelta_phase1_118/figures/seasonal_convergence_by_eval.png", "caption": "Seasonal convergence by evaluation for the selected runtime strategy (near-perfect in S0-S3)." }, { "filename": "seed_convergence_distribution_eval4.png", "url": "tests/pfdelta_phase1_118/figures/seed_convergence_distribution_eval4.png", "caption": "Per-seed convergence distribution at eval 4 (all 20 seeds at 1.0)." }, { "filename": "recovery_time_distribution_eval4.png", "url": "tests/pfdelta_phase1_118/figures/recovery_time_distribution_eval4.png", "caption": "Recovery-time distribution at eval 4 (all recorded events recover in 1 step)." } ], "created_at": "2026-02-17", "doi": "10.5281/zenodo.18716837", "zenodo_url": "https://doi.org/10.5281/zenodo.18716837" } ] }