Multifidelity Estimator

Multifidelity estimators leverage data from multiple sources of varying accuracy (fidelities) to improve the efficiency and robustness of machine learning models, particularly in computationally expensive scientific applications. Current research focuses on developing novel algorithms, such as those based on graph neural networks and Riemannian manifold regression, to effectively combine these data sources, often within Bayesian optimization or reinforcement learning frameworks. This approach is significant because it allows for the creation of accurate predictive models even when high-fidelity data is scarce and expensive to obtain, leading to substantial cost savings and improved model performance across diverse scientific domains.