AI-DAPT Project

Part of EU Open Research Repository
Published March 7, 2026 | Version v1
Book chapter Open

Hybrid Intelligence: The Fusion of Science-Based and Machine Learning Models

Authors/Creators

  • 1. Athena Research and Innovation Center In Information Communication & Knowledge Technologies
  • 2. Suite5 Data Intelligence Solutions Ltd
  • 3. UBITECH Limited
  • 4. DOMX IoT Technologies
  • 5. ROR icon University of Cyprus
  • 6. Consorzio Intellimech
  • 7. MCS Data labs
  • 8. Zenith Gas Supply Company
  • 9. OHS Engineering GmbH
  • 10. BIBA - Bremer Institut fur Produktion und Logistik GmbH
  • 11. UBITECH

Description

Process models and parameter estimation have long been fundamental tools in domains such as manufacturing, robotics, power plants, and biotechnology. Early modeling approaches relied on complex systems of mathematical differential and algebraic equations, known as science-based models, to capture process knowledge and scientific expertise. These models leverage physical and chemical properties, static and dynamic behaviors, and causal relationships among observed quantities to support predictive control and operational optimization. As white-box models, they provide transparency by uncovering the inner logic and decision-making steps of the process. However, the advent of Industry 4.0 has brought a surge in available data from industrial processes, driving the rapid growth of machine learning (ML) models. Those models excel at discovering patterns and nonlinear relationships in data, but often they are black-box models, i.e., they lack interpretability. To combine the strengths of science-based and ML models, hybrid models have emerged as a powerful solution. By integrating the transparency and domain knowledge of science-based approaches with the adaptability and predictive capabilities of ML, hybrid models enhance accuracy, robustness, and scalability. This chapter explores the foundations of hybrid models, their development, and applications, providing a comprehensive perspective on their transformative potential across various scientific and engineering domains. This work is done in the context of the AI-DAPT EU project.

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Additional details

Funding

European Commission
AI-DAPT - AI-Ops Framework for Automated, Intelligent and Reliable Data/AI Pipelines Lifecycle with Humans-in-the-Loop and Coupling of Hybrid Science-Guided and AI Models 101135826