We describe and compare two formulations of inverse problems for a physics-based process
model in the context of uncertainty and random variability: the Bayesian inverse problem …

The stochastic inverse problem (StocIP), which aims to align push-forward and observed
output distributions by estimating probability distributions of unknown system inputs, often …

A recently developed measure-theoretic framework solves a stochastic inverse problem
(SIP) for models where uncertainties in model output data are predominantly due to aleatoric …

The mechanisms and geographic distribution of global tidal dissipation in barotropic tidal
models are examined using a high resolution unstructured mesh finite element model. Mesh …

Research performed to-date on data assimilation (DA) in storm surge modeling has found it
to have limited value for predicting rapid surge responses (eg, those accompanying tropical …

We present a novel method for generating sequential parameter estimates and quantifying
epistemic uncertainty in dynamical systems within a data‐consistent (DC) framework. The …

Abstract Specification of hydraulic conductivity as a model parameter in groundwater flow
and transport equations is an essential step in predictive simulations. It is often infeasible in …

This study explores the discrepancies of storm surge predictions driven by the parametric
wind model and the numerical weather prediction model. Serving as a leading-order storm …

Due to increasing demand for food, intensive mariculture of finfish is a fast develo**
industry in the Iranian part of the Persian Gulf (PG). The environmental impacts of cage fish …

Dynamical systems arise in a wide variety of mathematical models from the physical,
engineering, life, and social sciences. A common challenge is to quantify uncertainties on …