Differentiable Digital Twins
Channel: NG-CDI
Date: 2022-08-04

Digital Twins are typically defined as virtual representations of physical things or processes. Digital Twin (DT) is a sandboxed environment, concurrently updated with the real-world processes or the network which it models.  More recently, DT have emerged as synthetic data generation tools for training deep learning algorithms. In this role, DT can provide potentially infinite amount of labelled data, limited only by the available computational resources. Furthermore, they are indispensable for training reinforcement learning agents. The sandboxed DT can train decision making policies in a safe environment, testing for vulnerabilities, and probing counterfactual decisions. In this lecture, Differentiable Digital Twins will be introduced. DDT are simulation environments which are constructed using deep neural networks. As a result, they can tap into deep learning hardware accelerators (GPU/ TPU /IPS) to achieve massive speed-up in performance. DDT can naturally fuse traditional DT designed by experts, with the data from the real environment or a network. 

Rob is a Professor at the University of Bristol. His research expertise is in the areas of Connected Intelligent Systems, Wireless & Self-Learning Networks, Information and Communication Theory, Statistics and AI. He has published over 200 papers in international journals and conferences and holds 13 patents in these areas.