Function Network

Function networks represent a powerful approach to modeling complex systems by decomposing a large problem into interconnected, smaller sub-problems. Current research focuses on efficient optimization strategies for these networks, including Bayesian optimization techniques that leverage intermediate results to reduce computational cost and algorithms designed for specific network architectures like cascades and feed-forward networks. This research is significant because it enables more efficient and effective modeling of intricate processes across diverse fields, from robotics control and engineering design to machine learning and scientific simulations.