Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation

摘要

Traditional model reduction derives reduced models from large-scale systems in a one-time computationally expensive offline (training) phase and then evaluates reduced models in an online phase to rapidly predict system outputs; however, this offline/onli