摘要：Numerical simulation of large-scale complex systems is challenging, especially when such a task needs
to be done many times under parameter variations, e.g., in the context of optimization, control, and
parameter estimation. Model order reduction (MOR) is a useful technique for constructing a low-cost
surrogate, i.e., a reduced order model (ROM), which can reproduce the input-output response of the
original large-scale system, with compromise on the accuracy to an acceptable extent. To compute
a ROM, an efficient a posteriori error estimation is crucial because it enables the computation to be
reliable and automatic.
