Multifidelity Surrogate Models: A New Data Fusion Perspective
This work addresses the problem of selecting fidelity levels and developing efficient data fusion methods for design optimization in various domains, representing an incremental improvement over existing techniques.
The paper tackles the challenge of efficiently constructing multifidelity surrogate models by proposing a new fusion approach that uses only gradients to build regression surfaces, demonstrated on foundational example problems to illustrate its efficacy.
Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computational resources, and high-fidelity models for detailed refinement. It improves decision-making by addressing uncertainties and surpassing the limits of single-fidelity models, which either oversimplify or are computationally intensive. Blending high-fidelity data for detailed responses with frequent low-fidelity data for quick approximations facilitates design optimisation in various domains. Despite progress in interpolation, regression, enhanced sampling, error estimation, variable fidelity, and data fusion techniques, challenges persist in selecting fidelity levels and developing efficient data fusion methods. This study proposes a new fusion approach to construct multi-fidelity surrogate models by constructing gradient-only surrogates that use only gradients to construct regression surfaces. Results are demonstrated on foundational example problems that isolate and illustrate the fusion approach's efficacy, avoiding the need for complex examples that obfuscate the main concept.