Researchers have developed a new bifidelity Karhunen-Loève expansion (KLE) surrogate model designed for field-valued quantities of interest under uncertain inputs. This model combines the efficiency of KLE with polynomial chaos expansions to maintain a clear link between input uncertainties and output fields. By integrating inexpensive low-fidelity simulations with a limited number of high-fidelity simulations, the method aims for accurate and cost-effective surrogate construction. An active learning strategy is also introduced to adaptively select high-fidelity simulations based on the surrogate's estimated generalization error, further enhancing prediction accuracy. AI
IMPACT This research introduces a novel method for improving the accuracy and efficiency of surrogate models in complex simulations, potentially impacting fields that rely on uncertainty quantification.
RANK_REASON This is a research paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=0.7]
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