In a data-driven finite element analysis the experimental data are directly employed as an input for computational analysis, thus evading any state of matter modeling. The essential physical principles, such balance laws and continuity, remain unchanged, do all numerical schemes used in their discretization. addition, uncertainties fluctuations experimentally measured enter simulation. Here is ...

We identify parameters of a given input-output model so that estimated model output is consistent with the measured output of the system modeled. Parameter estimation based on a set-membership approach is a nonprobabilistic method for characterizing the uncertainty with which each model parameter is known. The model is consistent with data if the estimated output domain contains measured system...

Dealing with uncertainty is one of the major problems in robotics and one of the main obstacles to populating the world with robots that do something useful. This paper offers a new method for modeling uncertainties that exist in & robotic system, bosed on stochaatic differential equations. The benefit of using such a model is that we axe then able to capture in a analytic mathemgtical structur...

We study the uncertainties of Sudakov form factors as the basis for parton shower evolution in Monte Carlo event generators. We discuss the particular cases of systematic uncertainties of parton distribution functions and scale uncertainties.

This paper has been presented at an expert meeting on CO2 capture technology learning at the IEA headquarters, January 24th 2003. The electricity sector is a key source of CO2 emissions and a strong increase of emissions is forecast in a business-as-usual scenario. A range of strategies have been proposed to reduce these emissions. This paper focuses on one of the promising strategies, CO2 capt...