Various industries, such as manufacturing, energy and utilities, automotive, aerospace and defense, logistics and transportation, and building management, have proposed the use of digital twins to aid the design, analysis, build, manufacturing and operations phases of asset-intensive industries. Digital twins have a physical asset, a virtual asset (a simulation model of the asset), and a two-way information flow between the physical and virtual worlds using an IOT platform. While most companies use data-based analytics and machine learning to build digital twins, they require a lot of training data, and the accuracy is limited to the observed data. Some industries have started using physics-based simulation to build digital twins, and while these approaches are accurate, they require long computation times to deploy. Most recently, companies are using hybrid approaches, combining data-based analytics and physics-based approaches, to build these digital twins that are very accurate and require less training data and drive high operational efficiency of assets and process industries and manufacturing plants. In this talk, we will discuss the challenges and opportunities of digital twins in various industries and the latest research approaches.