Standard Generative AI
Learns correlations from examples.
Unconstrained outputs can include non-physical states.
Augmentation samples transformations but does not encode an exact group action.
Geometry-native machine learning
Models that respect the space your physics inhabits.
For semiconductor design leads, computational chemists, and aerospace fluid dynamicists: encode the symmetries, boundary conditions, and geometric structure that ordinary generative models leave to chance.
Declared constraints can be verified. Accuracy, stability, and business value remain benchmark questions.
Designed for physical domains
- Semiconductor lithography: symmetry-aware surrogate models on layout and process geometry.
- Computational chemistry: SE(3)-equivariant molecular representations.
- Aerospace fluids: mesh-aware operators with declared boundary conditions.
Architecture comparison
Replace “looks plausible” with a checkable constraint boundary.
Maha Geometric AI
Uses equivariant graph networks and gauge-aware operators.
Strict guarantees for declared symmetries and encoded conditions.
Architecture preserves the specified transformation law by design.
“Zero physical hallucinations” applies only to violations of an explicitly encoded and correctly implemented condition; it is not a claim that every generated scientific result is correct.
A bounded engineering engagement
Start with the invariants that must not break.
- 01 / Declare group action, representation, boundary conditions, mesh topology, and acceptance metrics.
- 02 / Compare against a baseline on held-out geometric transformations and operational data.
- 03 / Inspect residuals, constraint checks, and cost—not a generalized benchmark claim.