We live in a world replete with complex system of systems that need to operate in a dynamic and uncertain environment thus necessitating continuous adaptation so as to deliver the stated goals which may also change over time. These systems, characterized by large size and non-linear interactions, are spread across spaces such as information-only, cyber-physical, societal, and biological. They raise new concerns to be addressed such as: (i) Why things are the way they are? (ii) What are the right interventions to bring the system back to the desired state? (iii) Is a better state possible? And (iv) How best to reach the desired to-be state from as-is state? With current practice found wanting, we present a digital twin centric simulation-based approach and associated technology that builds further upon proven results from Modelling & simulation, AI, Control Theory, and Software Engineering. We illustrate utility and efficacy of the approach with a representative sample of real world use cases and highlight open challenges that need to be overcome going forward. We also touch upon leveraging Generative AI in supporting Digital Twin life cycle.