Hybrid Digital Twin

Hybrid digital twins integrate physics-based models with data-driven approaches, such as machine learning, to create more accurate and adaptable representations of physical systems. Current research focuses on developing these hybrid models for applications like predictive maintenance and process plant optimization, often leveraging simulation environments to facilitate model building and validation. This approach aims to overcome limitations of solely relying on either physics-based or data-driven models, leading to improved system understanding, enhanced decision-making, and more efficient operations across various industries.