{"query": "Diffusion models — the probability-flow ODE behind generativ", "count": 20, "results": [{"id": "card_theory_kolmogorov_probability_axioms", "title": "Kolmogorov probability axioms", "shelf": "theories", "surface": "secular", "snippet": "Kolmogorov probability axioms — an engine domain that can touch it: probability. Calibration: seals. Three axioms (1933): probabilities are non-negative, the whole sample space has probability 1, and ", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_win_probability_models", "title": "Win-probability & expected-value models", "shelf": "theories", "surface": "secular", "snippet": "Win-probability & expected-value models — an engine domain that can touch it: sports_analytics. Calibration: partial — specific relations verify; the theory as a whole is not a sealable computation. E", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (lone-domain seeding)", "readable": false, "generated": false}, {"id": "card_theory_epidemiology_models", "title": "Compartmental epidemiology (SIR models and R₀)", "shelf": "theories", "surface": "secular", "snippet": "Compartmental epidemiology (SIR models and R₀) — an engine domain that can touch it: medicine. Calibration: seals — R0, herd-immunity thresholds and epidemic curves compute. Divide a population into S", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_fluid_mechanics", "title": "Fluid mechanics (Bernoulli, Reynolds, Navier–Stokes)", "shelf": "theories", "surface": "secular", "snippet": "Fluid mechanics (Bernoulli, Reynolds, Navier–Stokes) — an engine domain that can touch it: hydrology. Calibration: seals — continuity, Bernoulli and Reynolds number compute directly. CONTINUITY first:", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_hydrologic_cycle_open_channel_flow", "title": "Hydrologic cycle & open-channel flow (Manning, Darcy)", "shelf": "theories", "surface": "secular", "snippet": "Hydrologic cycle & open-channel flow (Manning, Darcy) — an engine domain that can touch it: hydrology. Calibration: seals — Manning velocity, Darcy flow and catchment volumes compute. Water circulates", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_null_hypothesis_significance_testing", "title": "Null-hypothesis significance testing (Fisher / Neyman–Pearson)", "shelf": "theories", "surface": "secular", "snippet": "Null-hypothesis significance testing (Fisher / Neyman–Pearson) — an engine domain that can touch it: statistics. Calibration: seals — a p-value recomputes exactly. Assume the null hypothesis, compute ", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_n_8530c72a2201", "title": "Fluid probability dynamics — one continuity equation across physics, geometry, and ML", "shelf": "science", "surface": "secular", "snippet": "One equation wears many clothes. Probability is a conserved fluid: the continuity equation\ndρ/dt + ∇·J = 0 (current J = ρv) says density is never created or destroyed — it only flows. The\nsame skeleto", "authority_tier": "engine_derived", "source": "Concordance assay — 2026-07-08", "readable": false, "generated": false}, {"id": "card_theory_dimensional_analysis", "title": "Dimensional analysis & similarity (Buckingham Π)", "shelf": "theories", "surface": "secular", "snippet": "Dimensional analysis & similarity (Buckingham Π) — an engine domain that can touch it: physics. Calibration: seals — dimensional consistency and Π-group counts check exactly. Any true physical equatio", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_bohr_quantum_model_of_the_atom", "title": "Bohr / quantum model of the atom", "shelf": "theories", "surface": "secular", "snippet": "Bohr / quantum model of the atom — an engine domain that can touch it: atomic. Calibration: seals — energy levels and shell capacities compute. Electrons occupy discrete orbits with QUANTISED energies", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_n_2be7f1c3f6e9", "title": "Diffusion models — the probability-flow ODE behind generative AI", "shelf": "science", "surface": "secular", "snippet": "Score-based generative models noise data toward a Gaussian and learn to reverse it. The\n'probability-flow ODE' is the deterministic fluid whose time-marginals match the noising SDE — the\nsame continui", "authority_tier": "engine_derived", "source": "Concordance assay — 2026-07-08", "readable": false, "generated": false}, {"id": "card_theory_statistical_mechanics", "title": "Statistical mechanics (Boltzmann — why the second law is a counting argument)", "shelf": "theories", "surface": "secular", "snippet": "Statistical mechanics (Boltzmann — why the second law is a counting argument) — an engine domain that can touch it: thermodynamics. Calibration: seals — Boltzmann distributions, partition functions an", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_statistical_learning_theory__bias_variance__generali", "title": "Statistical learning theory (bias-variance, generalization)", "shelf": "theories", "surface": "secular", "snippet": "Statistical learning theory (bias-variance, generalization) — an engine domain that can touch it: statistics. Calibration: map-only — bounds are asymptotic and assumption-laden. Why fitting the data y", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_bayes_theorem", "title": "Bayes' theorem", "shelf": "theories", "surface": "secular", "snippet": "Bayes' theorem — an engine domain that can touch it: probability. Calibration: seals. P(H|E) = P(E|H)·P(H) / P(E). How to update a belief when evidence arrives: posterior ∝ likelihood × prior. A trivi", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_theory_population_ecology", "title": "Population ecology (Lotka–Volterra, carrying capacity)", "shelf": "theories", "surface": "secular", "snippet": "Population ecology (Lotka–Volterra, carrying capacity) — an engine domain that can touch it: ecology. Calibration: seals — logistic and predator-prey models compute. Unlimited resources give exponenti", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_c_d7daafda1482", "title": "Fluid probability dynamics — one continuity  ↔ Diffusion models — the probability-flow ODE ", "shelf": "connections", "surface": null, "snippet": "machine learning (the probability-flow ODE under diffusion models)  — a concord the card itself states; mined + verified.", "authority_tier": "engine_derived", "source": "Concordance miner — 2026-07-11", "readable": false, "generated": false}, {"id": "card_theory_differential_equations", "title": "Differential equations (the language every physical law is written in)", "shelf": "theories", "surface": "secular", "snippet": "Differential equations (the language every physical law is written in) — an engine domain that can touch it: mathematics. Calibration: seals — solutions verify by substitution; specific integrations c", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_src_openstax_principles_managerial_accounting_11_4_use_discounted_cash_flow_models_to__c65c5121", "title": "11.4 Use Discounted Cash Flow Models to Make Capital Investment Decisions — Principles of Accounting, Volume 2: Managerial Accounting", "shelf": "economics", "surface": "secular", "snippet": "11.4\n\nUse Discounted Cash Flow Models to Make Capital Investment Decisions\n\nYour company, Rudolph Incorporated, has begun analyzing two potential future project alternatives that have passed the basic", "authority_tier": "reference", "source": "OpenStax: Principles of Accounting, Volume 2: Managerial Accounting (CC-BY 4.0)", "readable": false, "generated": false}, {"id": "card_theory_information_geometry", "title": "Information geometry (statistics as curved space)", "shelf": "theories", "surface": "secular", "snippet": "Information geometry (statistics as curved space) — an engine domain that can touch it: mathematics. Calibration: partial — specific relations verify; the theory as a whole is not a sealable computati", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (lone-domain seeding)", "readable": false, "generated": false}, {"id": "card_theory_power_laws", "title": "Power laws & heavy tails (Zipf, Pareto)", "shelf": "theories", "surface": "secular", "snippet": "Power laws & heavy tails (Zipf, Pareto) — an engine domain that can touch it: statistics. Calibration: seals — exponents, rank-frequency fits and tail probabilities compute. A distribution where P(x) ", "authority_tier": "reference", "source": "The Theory Assay — calibrated, not judged (docs/THEORY_CATALOG.md)", "readable": false, "generated": false}, {"id": "card_domchk_probability_claimed_binomial_probability", "title": "Probability: binomial probability", "shelf": "probability", "surface": "secular", "snippet": "A worked check in probability: binomial probability.\n\nGIVEN\n  binomial_k = 5\n  binomial_n = 10\n  binomial_p = 0.5\n\nCLAIMED\n  claimed_binomial_probability = 0.246094\n\nTHE ENGINE'S VERDICT\n  probability", "authority_tier": "reference", "source": "The verifier's own documented relation, and a run this engine performed against it (deterministic; re-runnable with tools/domain_goldens.py)", "readable": false, "generated": false}], "house": {"door": "FIND", "kind": "cards", "trail": "results", "seal": null, "next_step": {"do": "open the top card", "door": "FIND", "tool": "card_get", "params": {"id": "card_theory_kolmogorov_probability_axioms"}}, "ends": "a verdict or a card · the trail · a seal · one next step"}}