Fidelity Model

Fidelity models address the computational cost of high-fidelity simulations by integrating data from cheaper, lower-fidelity sources. Current research focuses on developing efficient data fusion techniques, often employing Bayesian neural networks, Gaussian processes, or Fourier neural operators, within multi-fidelity frameworks to improve accuracy and uncertainty quantification. This approach is significantly impacting diverse fields, from aerospace engineering and materials science to subsurface flow modeling and robotics, by enabling faster and more cost-effective simulations and optimization. The resulting improved accuracy and uncertainty estimates enhance decision-making in complex systems.