Similar System

Similar system research focuses on leveraging data from related systems to improve the efficiency and accuracy of modeling and control for a target system, particularly in data-scarce scenarios. Current efforts concentrate on techniques like meta-learning, Bayesian optimization, and knowledge transfer using pre-trained models, often employing neural networks or Gaussian processes to represent system dynamics and facilitate efficient data usage. This approach holds significant promise for accelerating system identification, optimizing control strategies, and enabling personalized models across various domains, ultimately reducing the need for extensive data collection on each individual system.