--- license: cc-by-4.0 language: - en tags: - physics - dimensional-analysis - buckingham-pi - vaschy-buckingham - symbolic-regression - science pretty_name: "Vashy: Dimensionally-Consistent Physics Equations" size_categories: - 1M `k₀` denotes the dimensionless constant of order unity that dimensional > analysis *cannot* determine (e.g. the ½ in kinetic energy, or 6π in Stokes' > drag). This is an intrinsic limitation of the method, not of the dataset. --- ## What is in the dataset The catalogue is **complete**: it contains *every* minimal dimensionless relation of size 2 to 8 over the quantity pool. Size 8 is a hard mathematical ceiling — with 7 SI base dimensions, no irreducible relation can involve more than 8 quantities (a relation of `k` quantities needs rank `k−1 ≤ 7`). | # quantities | rows | note | |---|---|---| | 2 | 22 | pairs with identical dimensions (e.g. energy vs torque) | | 3 | 593 | e.g. `F = k₀·m·a`, `E = k₀·m·v²` | | 4 | 17,325 | e.g. Reynolds, Weber, Strouhal numbers | | 5 | 280,646 | | | 6 | 813,919 | | | 7 | 1,140,445 | | | 8 | 72,370 | must span all 7 base dimensions at once (rare) | | **Total** | **2,325,320** | | Of these, **46,720 are flagged `plausible`** (a conservative gate for genuine physical candidates) and **20 correspond to classically named laws / numbers**. --- ## Provenance & method Every quantity is represented as a vector of integer exponents over the seven SI base dimensions. A **dimensionless relation** among a set of quantities is an integer null vector of the matrix whose columns are those dimension vectors. A row in this dataset is a **circuit** of that "dimensional matroid": a *minimal* dependent set, where every quantity is essential (removing any one destroys the dimensionless product) and which is not a product of smaller relations. Formally, a set of `n` quantities is a circuit iff its dimension matrix has rank `n−1` and the (unique) null vector has full support. Each circuit corresponds to exactly **one** irreducible dimensionless number, and the catalogue enumerates all of them without duplication. Dimensional analysis fixes only the *form* of a law up to `k₀`; it does not prove physical existence. That is why the dataset ships with relevance metrics rather than claiming every row is a real law. --- ## Dataset structure A single split (`default`) backed by `data/circuits.parquet` (~108 MB, zstd). | column | type | description | |---|---|---| | `size` | int | number of quantities in the relation (2–8) | | `keys` | string | sorted, comma-separated quantity keys (the relation's identity) | | `symbols` | string | the quantities' symbols, `·`-joined | | `domains` | string | distinct physics domains involved, comma-separated | | `pi` | string | the dimensionless number (Π group), e.g. `ℓ·v·ρ·μ⁻¹` | | `equation` | string | the relation solved for one quantity, e.g. `ℓ = k₀ · v⁻¹·ρ⁻¹·μ` | | `name` | string \| null | recognized name of the law / dimensionless number, if any | | `n_const` | int | number of universal constants (c₀, G, h, k_B, e, N_A) involved | | `n_domains` | int | number of distinct physics domains bridged | | `max_exp` | int | largest absolute exponent in the monomial | | `exp_l1` | int | sum of absolute exponents (monomial "weight") | | `complexity` | float | composite complexity score (lower = simpler) | | `score` | float | composite physicality score (higher = more plausible) | | `plausible` | bool | passes the conservative physicality gate (see below) | | `exponents` | string (JSON) | `{quantity_key: integer_exponent}` for the dimensionless monomial | ### The `plausible` gate A row is flagged `plausible = true` when it satisfies **all** of: - `size ≤ 5` — real laws rarely couple more than five quantities; - `n_const ≤ 1` — a genuine law uses at most one universal constant; - `n_domains ≤ 2` — at most one cross-domain bridge; - `max_exp ≤ 3` and `exp_l1 ≤ 8` — small integer exponents. This is intentionally conservative: it favours precision over recall. The remaining ~2.28 M rows are kept so that the catalogue stays complete and auditable, but the overwhelming majority are dimensional coincidences (e.g. `m = k₀ · c⁻¹·k_B`), not physical laws. ### The `score` A heuristic that rewards simplicity (few quantities, small exponents, single domain, at most one constant). Sorting `plausible` rows by `score DESC` surfaces textbook laws and named dimensionless numbers first. --- ## Example rows | equation | pi | name | size | score | plausible | |---|---|---|---|---|---| | `m = k₀ · a⁻¹·F` | `m·a·F⁻¹` | Newton 2nd law | 3 | 9.0 | ✅ | | `m = k₀ · v⁻²·E` | `m·v²·E⁻¹` | kinetic energy | 3 | 7.8 | ✅ | | `m = k₀ · E·c₀⁻²` | `m·E⁻¹·c₀²` | mass-energy (E=mc²) | 3 | 6.7 | ✅ | | `ℓ = k₀ · v⁻¹·ρ⁻¹·μ` | `ℓ·v·ρ·μ⁻¹` | Reynolds number | 4 | 8.0 | ✅ | | `E = k₀ · f·h` | `E·f⁻¹·h⁻¹` | Planck relation (E=hf) | 3 | — | ✅ | | `i = k₀ · U·R⁻¹` | `i·U⁻¹·R` | Ohm's law | 3 | — | ✅ | --- ## The quantity pool (60 quantities, 7 base dimensions) Base dimensions: **M** (mass), **L** (length), **T** (time), **Θ** (temperature), **I** (electric current), **N** (amount of substance), **J** (luminous intensity). ### Mechanics | symbol | quantity | dimensions | |---|---|---| | m | mass | M | | ℓ | length | L | | t | time | T | | v | velocity | L·T⁻¹ | | a | acceleration | L·T⁻² | | g | gravitational acceleration | L·T⁻² | | F | force | M·L·T⁻² | | E | energy | M·L²·T⁻² | | τ | torque | M·L²·T⁻² | | P | power | M·L²·T⁻³ | | p | momentum | M·L·T⁻¹ | | Lₐ | angular momentum | M·L²·T⁻¹ | | S | action | M·L²·T⁻¹ | | J | moment of inertia | M·L² | | P_p | pressure | M·L⁻¹·T⁻² | | ρ | mass density | M·L⁻³ | | μ | dynamic viscosity | M·L⁻¹·T⁻¹ | | ν | kinematic viscosity | L²·T⁻¹ | | ω | angular velocity | T⁻¹ | | f | frequency | T⁻¹ | | k | spring constant | M·T⁻² | | γ | surface tension | M·T⁻² | | A | area | L² | | V | volume | L³ | | Q | volumetric flow rate | L³·T⁻¹ | ### Thermodynamics | symbol | quantity | dimensions | |---|---|---| | Θ | temperature | Θ | | S_e | entropy | M·L²·T⁻²·Θ⁻¹ | | C | heat capacity | M·L²·T⁻²·Θ⁻¹ | | c | specific heat capacity | L²·T⁻²·Θ⁻¹ | | κ | thermal conductivity | M·L·T⁻³·Θ⁻¹ | | α | thermal diffusivity | L²·T⁻¹ | | β | thermal expansion coefficient | Θ⁻¹ | ### Electromagnetism | symbol | quantity | dimensions | |---|---|---| | q | electric charge | T·I | | i | electric current | I | | U | electric potential | M·L²·T⁻³·I⁻¹ | | R | electric resistance | M·L²·T⁻³·I⁻² | | C_e | capacitance | M⁻¹·L⁻²·T⁴·I² | | L_e | inductance | M·L²·T⁻²·I⁻² | | E_f | electric field | M·L·T⁻³·I⁻¹ | | B | magnetic flux density | M·T⁻²·I⁻¹ | | Φ | magnetic flux | M·L²·T⁻²·I⁻¹ | | ε | permittivity | M⁻¹·L⁻³·T⁴·I² | | μ₀ | permeability | M·L·T⁻²·I⁻² | | σ | electrical conductivity | M⁻¹·L⁻³·T³·I² | | ρ_e | electrical resistivity | M·L³·T⁻³·I⁻² | ### Chemistry | symbol | quantity | dimensions | |---|---|---| | n | amount of substance | N | | M_m | molar mass | M·N⁻¹ | | c_n | molar concentration | L⁻³·N | | E_m | molar energy | M·L²·T⁻²·N⁻¹ | | R | molar gas constant | M·L²·T⁻²·Θ⁻¹·N⁻¹ | | z | catalytic activity | T⁻¹·N | ### Photometry | symbol | quantity | dimensions | |---|---|---| | I_v | luminous intensity | J | | E_v | illuminance | L⁻²·J | | L_v | luminance | L⁻²·J | ### Universal constants | symbol | quantity | dimensions | |---|---|---| | c₀ | speed of light | L·T⁻¹ | | G | gravitational constant | M⁻¹·L³·T⁻² | | h | Planck constant | M·L²·T⁻¹ | | k_B | Boltzmann constant | M·L²·T⁻²·Θ⁻¹ | | e | elementary charge | T·I | | N_A | Avogadro constant | N⁻¹ | --- ## Loading ```python import pandas as pd df = pd.read_parquet("data/circuits.parquet") # the physically-plausible laws, best first laws = df[df.plausible].sort_values("score", ascending=False) # every relation involving viscosity, within mechanics df[df["keys"].str.contains("viscosity") & (df.domains == "mechanics")] ``` Or with the 🤗 `datasets` library: ```python from datasets import load_dataset ds = load_dataset("/vashy", split="train") ``` --- ## Intended uses - **Symbolic regression & scientific ML**: a dimensionally-valid hypothesis space / prior over candidate equations. - **Physics education**: browsing dimensionless numbers and their structure. - **Benchmarking**: testing whether models can recover known laws from dimensional constraints. ## Limitations & caveats - **Dimensional consistency ≠ physical truth.** Most rows are coincidences; use `plausible` and `score`, and validate against physics. - **The constant `k₀` is undetermined** by dimensional analysis. - **Only single dimensionless numbers are represented.** When a phenomenon needs two or more independent Π groups, the true law is `Π₁ = f(Π₂, …)` with an arbitrary function `f`; the dataset lists the individual irreducible Π groups (the circuits), not the functional relation between them. - **Pool-dependent.** The catalogue is exhaustive *for this 60-quantity pool*; a different pool yields a different catalogue. - Deliberate dimensional collisions in the pool (energy vs torque, action vs angular momentum, entropy vs heat capacity, acceleration vs gravity) are real and produce legitimate size-2 identities. ## License Released under **CC-BY-4.0**. The underlying facts are mathematical consequences of SI dimensional definitions. ## Citation ```bibtex @misc{r&d_mediation_2026, author = { R&D Mediation }, title = { PhysicsBabel (Revision 963a461) }, year = 2026, url = { https://huggingface.co/datasets/RANDMEDIATION/PhysicsBabel }, doi = { 10.57967/hf/9544 }, publisher = { Hugging Face } } ``` # Discovering equations by AI For instance, charge × flux = angular momentum $$S = k_0 \cdot q \cdot \Phi \qquad L_a = k_0 \cdot q \cdot \Phi$$ The product of charge × magnetic flux has the dimensions of an **action** and of an **angular momentum**: [C]·[Wb] = [J·s]. This isn't a coincidence — it's the backbone of three chapters of quantum physics: $$q\Phi/\hbar$$ est une phase → l'**effet Aharonov–Bohm** ; $$L_a = q\Phi$$: le champ électromagnétique... $$q\Phi = n\hbar$$ → la **quantification du flux**...