Multi Fidelity Data

Multi-fidelity data analysis tackles the challenge of integrating large amounts of inexpensive, low-accuracy data with smaller sets of expensive, high-accuracy data to improve overall model accuracy and efficiency. Current research focuses on developing sophisticated fusion methods, often employing Bayesian models, neural networks (including deep learning architectures like Fourier Neural Operators), and Gaussian processes, to effectively combine these data sources. These techniques are applied across diverse fields, from engineering simulations to scientific experiments, enabling more accurate predictions with reduced computational or experimental costs. The resulting improvements in predictive power and uncertainty quantification are significant for various applications, enhancing decision-making in areas like fluid dynamics, materials science, and optimization problems.