Core Technology: Physics-Informed AI (PI-AI)

The fundamental innovation of Geminus lies in its departure from pure data-driven artificial intelligence. In industrial environments, data is often "sparse" (limited sensors), "noisy" (measurement errors), or "unrepresentative" (missing extreme failure cases). Traditional AI models trained on such data often make "hallucinated" predictions that violate the laws of physics.

Geminus solves this by encoding the laws of physics directly into the neural network architecture. The platform treats the governing equations of a system?such as the Navier-Stokes equations for fluid flow or Maxwell?s equations for electromagnetics?as constraints on the learning process.

The Hybrid Loss Function

The platform utilizes a multi-component loss function to ensure accuracy. If $L$ is the total loss, it is typically expressed as:

$$L = L_{data} + \lambda L_{physics}$$

Where: * $L_{data}$ minimizes the difference between model predictions and observed sensor data. * $L_{physics}$ represents the residuals of the governing partial differential equations (PDEs), ensuring the model obeys conservation laws (mass, momentum, and energy). * $\lambda$ is a weighting factor that balances data-driven patterns with scientific rigor.

Key Advantages over Traditional ML:

• Data Efficiency: Models can be trained with up to 100x less data than pure machine learning approaches because the "physics" provides the necessary structure.
• Execution Speed: Once trained, Geminus models provide inference results up to $10^6$ times faster than traditional numerical solvers (CFD or FEA), enabling real-time "what-if" analysis.
• Interpretability: Because the models are constrained by first principles, the results are physically plausible and easier for professional engineers to trust in mission-critical applications.

The Geminus Platform

The core product is a scalable, generative engineering platform designed to handle the "system-of-systems" complexity found in modern industrial plants.

1. Generative Engineering Interface

The platform allows engineers to ingest CAD designs, SCADA data, and existing simulation models. It then automatically synthesizes these inputs into an "Evergreen" foundational model tailored to the specific enterprise assets.

2. High-Fidelity Digital Twins

Geminus creates dynamic digital twins that capture the transient behavior of physical systems. These twins are used for: * Predictive Maintenance: Identifying structural faults or process shifts before they lead to multi-step failures. * Autonomous Control: Delivering near-instantaneous set-point recommendations to optimize production networks (e.g., natural gas gathering or refinery units). * Uncertainty Quantification: Providing probabilistic bounds for every prediction, allowing decision-makers to assess the confidence level of the AI's output.

Industry Solutions and Applications

Oil and Gas

Geminus maintains a strategic partnership with SLB (Schlumberger) to deploy PI-AI across the energy value chain. * Flaring Reduction: Optimizing gas network throughput to eliminate the need for emergency flaring. * ESP Well Management: Enhancing production at the wellhead by accurately modeling the fluid dynamics of Electric Submersible Pumps (ESPs) in real-time. * Emission Control: Optimizing refinery unit set-points to increase production by up to 7% while lowering the carbon footprint.

Semiconductors

In the semiconductor sector, where precision is measured in nanometers, Geminus provides tools for: * Process Optimization: Real-time tuning of lithography, deposition, and etch processes. * Yield Management: Utilizing model-based control to prevent shifts in manufacturing yield, thereby reducing waste and accelerating time-to-market.

Utilities and Renewable Energy

• Grid Load Forecasting: Integrating generative AI with power grid data to predict demand and allocate resources with precision.
• Water Treatment: Optimizing chemical usage in water treatment processes, with case studies showing up to a 75% reduction in chemical costs.

Space and Defense

For mission-critical applications where data is extremely limited, Geminus provides situational awareness and reliability modeling for satellite operations and autonomous defense systems, navigating extreme uncertainties where traditional AI might fail.

Corporate History and Leadership

Geminus was founded by a team with deep roots in both Silicon Valley deep-tech and academic engineering research. * Greg Fallon (CEO): An experienced leader in industrial software, previously serving in senior roles at Autodesk. * Dr. Karthik Duraisamy (Founder & Chief Scientist): A Professor at the University of Michigan and Director of the Air Force Center of Excellence on Multi-fidelity Modeling. * Kamesh Raghavendra (Co-Founder): A venture capitalist and technologist with a background in scaling AI-driven infrastructure.

The company is supported by a strong base of institutional investors, including Lam Research Capital, SK Inc., The Hive, and Sentiero Ventures. As of 2026, Geminus is recognized as a pioneer in the "Industrial AI" movement, bridging the gap between scientific computing and real-time business intelligence.