As digitalization is permeating all sectors of society toward the concept of “smart everything,” and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives.

Designing a Cyber-physical system (CPS) including modeling the control components and services is a challenging issue. Models and simulations at run-time play a crucial role to implement these control and prediction components. Real-time constraints raise the complexity of designing an efficient CPS system. Having detailed models in making decisions and/or numerous predictions in different contexts is computationally expensive and difficult to schedule on the computational infrastructure. Inspired by substitutability, one strategy for dealing with complex CPS and the contradiction of better real-time performance and reduced cost in CPS is to employ approximated models and switch to the most suited model adaptively at run-time. However, using an approximate model raises the uncertainty on the model's predictions. Nonetheless, the model is appropriate when the uncertainty is within bound. This bound is defined as tolerance which is the permitted amount of uncertainty. In this paper, we propose a method for quantifying the tolerance of cyber-physical systems, where we can switch between the original model and approximated models and how to identify more appropriate models.

Designing a Cyber-Physical System (CPS), including modeling the control components and services, is a challenging task. Using models and simulations during run-time is crucial for successfully implementing advanced control and prediction components. The complexity of designing an effective CPS system increases due to real-time constraints. Generating accurate predictions and making decisions using detailed models in various contexts is %can be computationally demanding and complex to manage within the available computational resources. Employing approximated models and switching to the most suited model adaptively at run-time is an effective technique. But an approximated model is most probable not valid in all the different contexts the system will be in. This experience report uses the Validity Frame concept to enable %this adaptation at run-time. In each environment, some influencing factors are outside the model's control, but these properties influence the model's behavior. By defining Validity Frames, based on specific contexts and related models, we present a possible perspective to address the issue of selecting the more appropriate model in various contexts. Furthermore, we discuss the insights and lessons obtained and determine future challenges.

Model validity is as critical as the model itself, especially when guiding decision-making processes. Traditional approaches often rely on predefined validity frames, which may not always be available or sufficient. This paper introduces the Decision Oriented Technique (DOTechnique), a novel method for determining model validity based on decision consistency rather than output similarity. By evaluating whether surrogate models lead to equivalent decisions compared to high-fidelity models, DOTechnique enables efficient identification of validity regions, even in the absence of explicit validity boundaries. The approach integrates domain constraints and symbolic reasoning to narrow the search space, enhancing computational efficiency. A highway lane change system serves as a motivating example, demonstrating how DOTechnique can uncover the validity region of a simulation model. The results highlight the potential of the technique to support finding model validity through decision-maker context.