id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
5999n298x7t	Megan McCullough Hart	Data-Driven Models: From Data to Knowledge for Extreme Events	1904		.txt	text/plain	469	14	18	Specific areas of focus include: Develop improved methods for the detection of key features in datasets, such as non-stationarity, using the method of surrogates; Assess current analysis procedures utilized for both stationary and non-stationary wind datasets and introduce more flexible averaging interval schemes for the proper determination of turbulence characteristics in non-stationary wind; Develop an efficient multivariate non-Gaussian simulation approach that considers non-Gaussianity of the joint probability density function in addition to considering non-Gaussianity of the marginal distributions; Improve upon existing non-stationary simulation approaches by also allowing for consideration of non-Gaussianity; and Develop a more flexible and robust approach for the detection and analysis of nonlinear coupling between two processes related to hazard loading/response or within the same process between two different modes. In the area of dynamic load effects, advanced analysis, modeling, and simulation tools are becoming increasingly important in order to address the non-stationarity, non-Gaussianity, and nonlinearity inherent in hazard related events and their associated structural response.	cache/5999n298x7t.txt	txt/5999n298x7t.txt