Geminus

Executes physics-informed machine learning models that solve complex engineering simulations (FEA, CFD, structural analysis) orders of magnitude faster than traditional numerical methods. Integrates domain-specific physics constraints to ensure physically plausible outputs.

Generates predictions and solutions for physics-heavy problems with significantly lower hallucination rates compared to general-purpose LLMs. Physics constraints act as guardrails to keep outputs within physically valid solution spaces.

Seamlessly connects with existing CAD systems to enable AI-driven design optimization and parametric exploration. Automatically translates between CAD geometry and physics-informed models for iterative design refinement.

Models and predicts manufacturing process outcomes (molding, casting, machining, assembly) using physics-informed AI. Enables virtual process validation and optimization before physical production.

Predicts material properties and behavior under various conditions using physics-informed models trained on materials science data. Enables rapid material selection and performance forecasting without extensive lab testing.

Validates engineering designs and simulations against regulatory requirements and safety standards using physics-informed models. Reduces compliance validation time by automatically checking against known constraints and standards.

Analyzes and optimizes energy consumption in engineering systems and processes using physics-informed models. Identifies efficiency improvements and predicts energy impact of design changes.

Predicts performance characteristics of prototype designs before physical fabrication using physics-informed models. Enables design validation and iteration acceleration in early development stages.