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Physical Combinatorics
An innovation discovery engine that explores the combinatorial space of physical systems — generating, constraining, scoring, and ranking entity combinations against real-world physics.
An innovation discovery engine that explores the combinatorial space of physical systems — generating, constraining, scoring, and ranking entity combinations against real-world physics.
PhysCom takes entities from different dimensions, forms their Cartesian product, then filters and scores every combination through a 5-pass pipeline.
Each entity declares physical dependencies — what it requires, provides, or excludes. Incompatible combinations are blocked before any estimation begins.
vacuum_or_thin atmosphere —
Human Pedalling requires none fuel infrastructure
Surviving combinations get raw metric estimates — power density, cost, range — from a deterministic physics engine that sizes each combination from its own declared attributes, not a guess.
Raw estimates are log-normalized against domain-specific bounds, then combined into a single composite score via weighted geometric mean. Combinations are ranked within their domain.
urban_commuting weights: power density 42%,
cost 42%, range 17%
Top-scoring combinations are sent to a language model for a plausibility verdict plus a holistic LOW/MEDIUM/HIGH rating — weighing safety and accessibility as qualitative judgment calls alongside the physics scores, catching physically valid but practically absurd pairings.
The final pass surfaces results for expert evaluation. Reviewers can approve, flag, or annotate each combination with domain knowledge that no model captures.
The building blocks. Each entity belongs to a dimension (e.g. platform, actuator, energy_storage) and carries typed dependencies that define its physical properties and constraints.
The evaluation lens. A domain defines which metrics matter and their normalization bounds — the same combination scores differently under "urban commuting" vs "interplanetary travel."
The physics layer. Typed key-value constraints
(requires, provides, excludes,
range_min, range_max) that gate which
combinations are physically possible.
Quantitative physics axes like power density, cost, and range. Each metric has a domain-specific weight and normalization range. Some are inverted — lower cost is better. Safety and accessibility are judgment calls, not physics quantities — they're weighed qualitatively in the LLM review pass instead of scored here.