Algorithm accounts for uncertainty to enable more accurate modeling
A notable error source in modeling physical systems is parametric uncertainty, where the values of model parameters that characterize the system are not known exactly due to limited data or incomplete knowledge. In this situation, a data assimilation algorithm can improve modeling accuracy by quantifying and reducing such uncertainty. However, these algorithms often require a large number of repetitive model evaluations that incur significant computational resource costs. In response to this issue, PNNL's Dr. Weixuan Li and Professor Guang Lin from Purdue University have proposed an adaptive importance sampling algorithm that alleviates the burden caused by computationally demanding models. In three test cases, they demonstrated that the algorithm can effectively capture the complex posterior parametric uncertainties for the specific problems being examined while also enhancing computational efficiency.