## 3 Recent Literature Analysis
### Cyber-Physical Systems
#### 3.2.1 Simulation and Modeling  
Simulating and modeling cyber-physical systems was one significant component of the most recent novel CPS research. This area was divided into two primary focus areas, with these being digital twin applications, and standards and realism considerations. Of note, much of the literature within this section focused on Industry 4.0 applications, such as manufacturing and agriculture. This poses a significant research niche, but also brings forth questions regarding the wider CPS cross-applicability of findings. Additional questions are also posed by the significant focus on digital twins for simulation purposes, with research challenges identified regarding the accuracy of twin architecture and flexibility in the scope of their design. Digital twins were the primary form of simulation identified within novel research for CPS. One application of digital twin-based manufacturing is system design platforms utilizing emerging architectures such as configuration design, motion planning, control development, and optimization decoupling (CMCO) [37]. Such systems present novelty in  
Figure 3: CPS recent novel research clusters. their work through the integration of hardware-in-the-loop simulation, in order to reduce errors, increase efficiency, and verify execution [37]. Use cases such as mass-individualization as part of Industry 4.0 were identified as drivers for digital twin optimization to maximize efficiency of product-service systems; however, it is noted that robustness is required in such models in order to address unpredictable behaviors such as physical disturbances [38]. In a different study, digital twins were applied to a CPS manufacturing context and compared to conventional assembly process design methods [39]. The benefits of this application of digital twins included the streamlining of assembly processes whilst maintaining flexibility and reducing costs [39]. Much of the novel recent CPS literature that focused on digital twins considered them primarily within a manufacturing or Industry 4.0 context. Whilst a significant area of research, this literature focus on one use case reveals potential opportunities for digital twin cross-application outside of the manufacturing context, which may pose new challenges and design evolutions for CPS modeling. Applications of digital twins bring forth further challenges regarding the accuracy of digital twin models, especially in regards to the granularity of behavior and quality of replication, and challenges regarding data scalability and resource processing [39]. Modeling digital twins so that they are valid and useful within CPS environments is subsequently of the utmost importance, with some research focusing on developing virtual modeling methods to enable effective digital twin development [40]. The value of a digital twin model relies on the degree of similarity between the digital twin itself, and its real-world subject, with inaccurate models potentially influencing the accuracy of CPS simulations and outcomes. This is an area where future work is required, especially when conceptualized within predictive simulation contexts where future states can only be evaluated against their real-world events, assuming that those events occur. One technique discussed within the literature addressed this simulation accuracy challenge through the development of a modeling approach that included geometric, physical, behavioral, rule-based, and data-based dimensionalities [41]. Additionally, building robustness and resilience to digital twins is imperative as systems become potentially more complex and unpredictable, as they need to be flexible enough to adapt to varying system states and potentially emergent behaviors. Data mining is also identified as a potential future area of work, where hidden relationships in CPS data can be used to inform more accurate digital twin models [41] Building frameworks is an additional area of CPS that assists in modeling how systems operate.