Non Intrusive

Non-intrusive methods aim to analyze and model systems without direct access to their internal workings, relying instead on external observations or readily available data. Current research focuses on developing data-driven reduced-order models using techniques like neural networks, quantum reservoir computing, and generative adversarial networks, often within frameworks like digital twins, to improve efficiency and prediction accuracy in diverse applications. These advancements are significant for accelerating simulations, enabling real-time predictions in complex systems (e.g., climate modeling, circuit design, and industrial processes), and enhancing the development of efficient and effective digital twins.