Accuracy & Data Transparency
At PhysicsGPT, accuracy is not merely an algorithm setting—it is an academic commitment. This page outlines how our AI reasoning engine derives solutions, what principles govern our knowledge base, and how users should responsibly evaluate results.
How PhysicsGPT Derives Answers
PhysicsGPT is designed as a logic-first pedagogical engine. Rather than outputting statistical approximations, our prompt architectures enforce rigorous adherence to physical laws, dimensional homogeneity, and SI unit conventions.
- Dimensional consistency verification on all derived equations.
- Multi-step algebraic decomposition with explicit substitutions.
- Standard physical constants (CODATA standard values).
Pedagogical Reasoning vs Simple Calculators
Standard online calculators simply evaluate expressions. PhysicsGPT contextualizes why a particular law applies (e.g., distinguishing between conservative and non-conservative work, laminar vs turbulent fluid flow, or elastic vs inelastic collisions).
Data Sources & Knowledge Foundation
PhysicsGPT does not access private student records, proprietary paywalled exams, or scraped personal data. Its mathematical and physical knowledge is established upon peer-reviewed open scientific literature, standardized university physics curricula, and verified textbook fundamentals.
Classical Mechanics
Newtonian dynamics, Lagrangian principles, rotational mechanics, kinematics, and harmonic motion.
Electrodynamics
Maxwell's equations, Coulomb's law, Biot-Savart law, electromagnetic induction, and AC circuits.
Thermodynamics & Optics
Entropy, heat engines, Carnot cycles, wave optics, diffraction, and quantum Planck relations.
While PhysicsGPT strives for maximum precision, small ambiguities in problem wording, unspecified frictional coefficients, or non-standard reference frames may impact numerical derivations. PhysicsGPT is intended as a learning and study aid, and should complement rather than replace instructor guidance, peer review, and laboratory experiments.