Model Based

Model-based approaches in various scientific fields aim to leverage explicit models of systems or processes to improve efficiency and robustness in tasks like control, prediction, and decision-making. Current research emphasizes developing and refining model architectures, such as Gaussian processes, coupled oscillator networks, and neural networks (including physics-informed and sparse variants), often integrated with algorithms like model predictive control and active learning to optimize data usage and performance. These advancements are significant because they enable more efficient and reliable solutions in diverse applications ranging from robotics and autonomous systems to engineering design and healthcare.