Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis

Efficient methods for achieving active learning in complex physical systems are essential for achieving the two-way interaction between data and models that underlies DDDAS. This work presents a two-stage multifidelity active learning method for Gaussian-process-based reliability analysis. In the first stage, the method allows for the flexibility of using any single-fidelity acquisition function for failure boundary identification when selecting the next sample location. We demonstrate the generalized multifidelity method using the existing acquisition functions of expected feasibility, U-learning, targeted integrated mean square error acquisition functions, or their a priori Monte Carlo sampled variants. The second stage uses a weighted information-gain-based criterion for the fidelity model selection. The multifidelity method leads to significant computational savings over the single-fidelity versions for real-time reliability analysis involving expensive physical system simulations.