Energy Budget

Energy budget research focuses on quantifying and predicting energy consumption across diverse systems, from cellular networks and robotic systems to fluid dynamics. Current efforts utilize machine learning models, including recurrent neural networks (RNNs) and graph neural networks (GNNs), to improve prediction accuracy and efficiency, often incorporating novel training methods and attention mechanisms. These advancements are crucial for optimizing energy efficiency in various applications, ranging from improving the sustainability of communication technologies to enhancing the performance of autonomous robots and refining simulations of complex physical processes. The development of robust calibration tools further strengthens the reliability and applicability of energy budget models.