Developing a modern technical product such as a car, plane, or a complex medical device includes designing the complex interplay between sensors (which measure physical product and environment state) and actuators (such as small electric motors that control the product).

Cyber-Physical Systems (CPS) are composed of a wide range of networked physical, computational, and human/organization components. These systems are highly complex as they have many different heterogeneous components, such as physical, computational, and human. Simulation-based evaluation of the behavior of CPS is complex, as it involves multiple, heterogeneous, interacting domains. Each simulation domain has sophisticated tools, but their integration into a coherent framework is a difficult, time-consuming, labor-intensive, and error-prone task.

Recent advances in machine learning have already had a transformative impact on our lives. However, astonishing successes in diverse domains, such as image classification, speech recognition, self-driving cars and natural language processing, have mostly been driven by commercial forces, and these techniques have not yet been widely transitioned into various science domains.

Cyber-physical systems (CPS) are engineered systems created as networks of interacting physical and computational processes. Most modern products in major industrial sectors, such as automotive, avionics, medical devices, and power systems already are or rapidly becoming CPS driven by new requirements and competitive pressures.

Automated design processes, especially using Machine Learning/AI techniques, require proposed systems to be evaluated across all relevant attributes, requirements, and concerns.  Traditionally, teams create models in a set of engineering tools for design evaluation data.