The economics of climate change involves a vast array of uncertainties, complicating our understanding of climate change. This study explores uncertainty in baseline trajectories using multiple integrated assessment models commonly used in climate policy development. The study examines model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It esti...

The quantification of model uncertainty becomes increasingly important as robust control is an important tool for control system design and analysis. This paper presents an algorithm to characterize the model uncertainty in terms of parametric and nonparametric uncertainties directly from inputloutput data. We focus on the quantification of parametric uncertainty, which is represented as an int...

In this paper we use the parameter-dependent Lyapunov function framework developed by Haddad and Bernstein to address the problem of robust stabilization for systems with parametric uncertainty and system delay. The principal result involves the construction of a modified Riccati equation for characterizing a memoryless (delay-independent) feedback controller that guarantees robust stability in...

This paper introduces a method to minimize norms on nonlinear trajectory sensitivities during open-loop trajectory optimization. Specifically, we derive new parametric sensitivity terms that measure the variation in nonlinear (continuous-time) trajectories due to variations in model parameters, and hybrid sensitivities, which account for variations in trajectory caused by sudden transitions fro...

Abstract: In this paper we study the problem of H∞ control of singular linear continuous-time systems with parametric uncertainty. The system under consideration is subjected to time-delay in state, and norm-bounded parametric uncertainty entering all matrices of the system and output equations. First, the problem of robust stabilization of the underlying system is investigated. Next, we addres...

In this paper, the robust absolute stability property of multi-agent systems by transforming the original system into a Lur’e system is presented. Different configurations of multi-agent systems are considered, which they include parametric uncertainty. These systems are primarily stabilized by applying robust design techniques to further verify the absolute stability property of the closed loo...

Aim To examine the biogeographical history of the angiosperm clade Caprifolieae (Caprifoliaceae) using parametric biogeographical reconstruction methods. The existing parametric method was extended to evaluate biogeographical reconstructions over the distribution of dated phylogenies. This method provides a framework for reconstructing large-scale biogeography with parametric methods, while acc...

We examine a conceptual framework for accounting for all sources of uncertainty in complex prediction problems, involving six ingredients: past data, future observables, and scenario, structural, parametric, and predictive uncertainty. We apply this framework to nuclear waste disposal using a computer simulation environment GTNCHEN which "deterministically" models the one-dimensional migration ...

Metamodel-based robust design methods are commonly used to mitigate the influence of parametric uncertainty associated in sheet gauges and material properties in crashworthiness-based vehicle lightweight design. Since the crash performances are highly nonlinear and high-dimensional responses, the prediction error of metamodels inevitably introduces the so-called metamodeling uncertainty in robu...

Uncertainty analysis of greenhouse gas (GHG) emissions is becoming increasingly necessary in order to obtain a more accurate estimation of their quantities. The Monte Carlo simulation (MCS) and non-parametric block bootstrap (BB) methods were tested to estimate the uncertainty of GHG emissions from the consumption of feedstuffs and energy by dairy cows. In addition, the contribution to variance...