PrincipleDeducer is how we turn reasoning into execution: from plain-language physics problem, to derived method, to verified code, to a result the universe will accept.
Three commitments that make PrincipleDeducer different from an autocomplete for physicists.
Most AI coding tools pattern-match from training data. PrincipleDeducer will derive the method from the physics itself — and keep the derivation on the audit trail.
Every generated step is verified before the next one is written. Dimensions, Hermiticity, symmetries, limiting cases. Plausible is not good enough.
Code that merely runs is not the goal. Code whose results match the universe is. Every run is compared to published data, with the deviation quantified.
PrincipleDeducer is our flagship AI complex for believable nuclear physics coding. It takes a physics problem in plain language, formalizes the specification, derives the method, generates the code, verifies dimensions and conservation laws, runs it on real solvers, and validates the result against experiment.
Every step is auditable. Every claim is checked. Reasoning becomes execution — and execution becomes believability.
We are building it carefully and will release it only when every stage of the pipeline meets our standard for believability. No public playground, package, or API is available yet. Reach out if you would like to follow the work.