Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an impact on power system dynamics; However, except from the slow Monte Carlo simulation, most existing methods for quantifying this are intrusive, meaning they not based commercial simulation software and hence difficult use utility companies. To fill gap, paper proposes efficie...

This paper presents an efficient multiscale stochastic framework for uncertainty quantification in modeling of flow through porous media with multiple rock types. The governing equations are based on Darcy’s law with nonstationary stochastic permeability represented as a sum of local Karhunen-Loève expansions. The approximation uses stochastic collocation on either a tensor product or a sparse ...

Eukaryotic genome replication is stochastic, and each cell uses a different cohort of replication origins. We demonstrate that interpreting high-resolution Saccharomyces cerevisiae genome replication data with a mathematical model allows quantification of the stochastic nature of genome replication, including the efficiency of each origin and the distribution of termination events. Single-cell ...

We review the topic of stochastic epidemic modeling with emphasis on compartmental stochastic models. A main theme is the usefulness of the correspondence between these and their large population deterministic limits, which describe dynamical systems. The dynamics of an ODE system informs us of the deterministic skeleton upon which the behavior of corresponding stochastic systems are built. In ...

We evaluate the performance of global stochastic collocation methods for solving nonlinear parabolic and elliptic problems (e.g., transient and steady nonlinear di↵usion) with random coe cients. The robustness of these and other strategies based on a spectral decomposition of stochastic state variables depends on the regularity of the system’s response in outcome space. The latter is a↵ected by...

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrog...

Most currently proteomic studies use data-dependent acquisition with dynamic exclusion to identify and quantify the peptides generated by the digestion of biological sample. Although dynamic exclusion permits more identifications and higher possibility to find low abundant proteins, stochastic and irreproducible precursor ion selection caused by dynamic exclusion limit the quantification capabi...

Multiple sources of errors and uncertainty arise in mechanics computational models and contribute to the uncertainty in the final model prediction. This paper develops a systematic error quantification methodology for computational models. Some types of errors are deterministic, and some are stochastic. Appropriate procedures are developed to either correct the model prediction for deterministi...