In a previous blog, What is Industrial AI? (And Why Most Companies Get It Wrong), we defined Industrial AI as the comprehensive and purpose-built application of artificial intelligence technologies to address the unique challenges and opportunities of industrial operations.
Importantly, Industrial AI encompasses the entire AI value chain, from establishing a contextualized data foundation to deploying advanced AI agents. This is because deploying effective artificial intelligence solutions within complex industrial environments presents unique challenges. It requires the seamless integration of diverse data sources (structured and unstructured OT, IT, ET data), the application of specialized Industrial AI techniques, and robust deployment across various operational landscapes.
We previously highlighted how Cognite's purpose-built platform directly addresses these complexities by providing the most comprehensive suite of Industrial AI capabilities:
- Advanced Analytics & Machine Learning: For predictive maintenance, anomaly detection, and process optimization.
- Physics-Based Hybrid AI: Combining data-driven AI with physics-based models for enhanced accuracy and explainability.
- Generative AI: Enabling natural language interaction with industrial data, document intelligence, and synthetic data generation.
- Agentic AI: Building intelligent Industrial AI agents to automate tasks and optimize complex workflows.
Discover how Cognite infuses AI at every layer, from data to decision.
In this blog, we'll break down how Cognite enables physics-based Hybrid AI.
Physics-based hybrid AI
For sensitive industrial applications where deep understanding and explainability are paramount, Cognite offers physics-guided machine learning. This hybrid approach to Industrial AI intelligently combines the powerful pattern recognition capabilities of data-driven machine learning with the fundamental principles of physics-based modeling. These sophisticated models provide unparalleled insights into complex physical processes, enabling real-time operational optimization and enhanced safety.
Here's how this capability transforms industrial operations:
- Seamless Integration with Leading Industrial Simulators: Connect directly with industry-standard simulators such as Unisim, Olga, Turbulent Flux, Multiflash, and Hysys. This leverages live, contextualized industrial data to significantly enhance simulation accuracy and relevance, bridging the gap between theoretical models and real-world conditions.
- Accelerate Industrial Innovation Cycles: Rapidly test new designs and operating conditions virtually, automate routine simulator studies, and proactively monitor critical problem cases with real-time data integration. This dramatically accelerates the pace of innovation and reduces time-to-market for new solutions.
- Enhance Industrial AI Explainability and Trust: Improve the accuracy and reliability of AI models by incorporating deep domain expertise. This fosters an understanding of the "why" behind AI predictions, leading to greater trust and confidence in Industrial AI-driven decisions within critical operations.
- Deploy Physics-Guided Machine Learning at Industrial Scale: Leverage cloud-connected Physics Simulators as a Service to generate valuable synthetic data and fast-track the implementation of virtual Industrial IoT (IIoT) sensors. This enables scalable deployment across your entire industrial asset base, unlocking new levels of operational efficiency and insight.
Learn how advances in AI make Asset Performance Management 2.0 a reality.
Summary
Cognite's Physics-Based Hybrid AI offers a powerful solution for industries demanding high accuracy and explainability. By intelligently merging data-driven insights with fundamental physics, it not only enhances operational understanding and safety but also significantly accelerates innovation and fosters greater trust in AI-driven decisions across critical industrial applications.
Learn more about Cognite's approach to industrial AI: