Multi Fidelity

Multi-fidelity methods address the computational cost of high-fidelity simulations by integrating them with cheaper, lower-fidelity approximations. Current research focuses on developing efficient surrogate models, often employing Bayesian neural networks, Gaussian processes, or neural operators, to fuse data from multiple fidelity levels and improve prediction accuracy while minimizing computational expense. These techniques are proving valuable across diverse fields, including engineering design, materials science, and scientific computing, by enabling faster and more cost-effective optimization, uncertainty quantification, and model development. The resulting improvements in efficiency and accuracy are significantly impacting the feasibility of complex simulations and design processes.