Traditional AI systems in industrial settings often require extensive data and computational resources, leading to long deployment times and delayed return on investment. Physics-based simulations, while predictive, demand deep domain expertise and are computationally intensive, limiting their use for real-time operational decisions.

Geminus offers a physics-informed AI platform that combines physics-based simulations with deep learning to enable real-time operational decisions in complex industrial environments. By integrating these approaches, Geminus significantly reduces the time to achieve ROI, enhancing predictive accuracy and minimizing resource demands. The platform creates dynamic system digital twins, enabling users to explore "what-if" scenarios and receive control recommendations. Geminus's models leverage system physics for training, resulting in more accurate predictions compared to models relying solely on process data.

Physics-informed AI models that integrate simulation with deep learning

Dynamic System Digital Twins for real-time scenario exploration

Model Predictive Control for widespread automation

Autonomous Systems capabilities for self-optimization and self-healing

Uncertainty quantification for trustworthy recommendations

Rapid model creation, deployment, and retraining

Compatibility with various industrial sectors, including Oil & Gas, Space, Defense, Semiconductors, Utilities, and Renewable Energy

Geminus primarily targets industrial enterprises seeking to optimize complex systems, including those in the oil and gas, space, defense, semiconductor, utilities, and renewable energy sectors.