We present a review of methods for optimal experimental design (OED) for Bayesian inverse
problems governed by partial differential equations with infinite-dimensional parameters …

Highlights•Systematically review 82 model-based design (MBD) toolkits.•Dynamic,
multiscale, and interdisciplinary grand challenges drive software trends.•Organize MBD …

Derivation of reduced order representations of dynamical systems requires the modeling of
the truncated dynamics on the retained dynamics. In its most general form, this so-called …

Active non-line-of-sight imaging systems are of growing interest for diverse applications. The
most commonly proposed approaches to date rely on exploiting time-resolved …

Identifying unknown Young's moduli and boundary conditions of the heterogeneous material
using locally observed boundary data is the inverse problem which is generally solved by …

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems
governed by partial differential equations (PDEs) under model uncertainty. Specifically, we …

We consider optimal design of infinite-dimensional Bayesian linear inverse problems
governed by partial differential equations that contain secondary reducible model …

Differential equations have proven to be a powerful mathematical tool in science and
engineering, leading to better understanding, prediction, and control of dynamic processes …

In this study, we address three important challenges related to the COVID-19 pandemic,
namely,(a) providing an early warning to likely exposed individuals,(b) identifying …

In this study, we address three important challenges related to disease transmissions such
as the COVID-19 pandemic, namely,(a) providing an early warning to likely exposed …