
TL;DR
This paper discusses the development and application of real-time Digital Twins to enhance decision making and optimization in complex, dynamic industrial environments, leveraging integrated model-based and data-based technologies.
Contribution
It introduces real-time Digital Twins for online prediction and optimization, combining various technological approaches to improve industrial process management.
Findings
Real-time Digital Twins enable better prediction and control of industrial assets.
Integration of model-based and data-based approaches enhances performance.
Potential to overcome data limitations in industrial IoT applications.
Abstract
We live in a world of exploding complexity driven by technical evolution as well as highly volatile socio-economic environments. Managing complexity is a key issue in everyday decision making such as providing safe, sustainable, and efficient industrial control solutions as well as solving today's global grand challenges such as the climate change. However, the level of complexity has well reached our cognitive capability to take informed decisions. Digital Twins, tightly integrating the real and the digital world, are a key enabler to support decision making for complex systems. They allow informing operational as well as strategic decisions upfront through accepted virtual predictions and optimizations of their real-world counter parts. Here we focus on real-time Digital Twins for online prediction and optimization of highly dynamic industrial assets and processes. They offer…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Transformation in Industry · Machine Learning in Materials Science
