Original title: Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry
Authors: Carine Menezes Rebello, Johannes Jäschkea, Idelfonso B. R. Nogueira
In this article, a team explores the power of digital twins—a virtual mirror of real-world systems—for making smarter decisions in the oil and gas industry. These twins offer a window into the physical world, continuously learning and sharing precise information. However, implementing AI in real-time scenarios poses challenges. To tackle this, the team develops a robust framework blending Bayesian inference, simulations, and unique strategies, empowering the digital twin with cognition. By reducing complexity and managing uncertainty, this framework promises efficient and trustworthy decision-making. It fills gaps in existing literature by integrating diverse learning techniques and uncertainty handling. The goal? A reliable system that adapts to change, factors in prediction uncertainty, and enhances decision-making in complex, real-world situations. Ultimately, this groundwork could spark advancements in digital twins across industries, revolutionizing how we engineer and optimize processes.
Original article: https://arxiv.org/abs/2311.12755