Single Fidelity

Single-fidelity methods in optimization and modeling rely on a single source of data, often limiting efficiency and accuracy. Current research emphasizes multi-fidelity approaches, which integrate data of varying quality and cost (e.g., low-fidelity approximations and high-fidelity, computationally expensive simulations), leveraging Bayesian optimization and Gaussian processes to improve model accuracy and reduce computational burden. This shift towards multi-fidelity techniques is significantly impacting fields like materials science, drug discovery, and machine learning by enabling faster and more cost-effective optimization and design processes. Active research focuses on developing efficient data fusion methods and adaptive sampling strategies to optimize the use of diverse data sources.