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Full-Text Articles in Probability
Nonparametric Bayesian Deep Learning For Scientific Data Analysis, Devanshu Agrawal
Nonparametric Bayesian Deep Learning For Scientific Data Analysis, Devanshu Agrawal
Doctoral Dissertations
Deep learning (DL) has emerged as the leading paradigm for predictive modeling in a variety of domains, especially those involving large volumes of high-dimensional spatio-temporal data such as images and text. With the rise of big data in scientific and engineering problems, there is now considerable interest in the research and development of DL for scientific applications. The scientific domain, however, poses unique challenges for DL, including special emphasis on interpretability and robustness. In particular, a priority of the Department of Energy (DOE) is the research and development of probabilistic ML methods that are robust to overfitting and offer reliable …
Mapping Spatial Thematic Accuracy Using Indicator Kriging, Maria I. Martinez
Mapping Spatial Thematic Accuracy Using Indicator Kriging, Maria I. Martinez
Masters Theses
Thematic maps derived from remote sensing imagery is increasingly being used in environmental and ecological modeling. Spatial information in these maps however is not free of error. Different methodologies such as error matrices are used to assess the accuracy of the spatial information. However, most of the methods commonly used for describing the accuracy assessment of thematic data fail to describe spatial differences of the accuracy across an area of interest. This thesis describes the use of indicator kriging as a geostatistical method for mapping the spatial accuracy of thematic maps. The method is illustrated by constructing accuracy maps for …
Automating Large-Scale Simulation Calibration To Real-World Sensor Data, Richard Everett Edwards
Automating Large-Scale Simulation Calibration To Real-World Sensor Data, Richard Everett Edwards
Doctoral Dissertations
Many key decisions and design policies are made using sophisticated computer simulations. However, these sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. This dissertation's goal is to address these simulation deficiencies by presenting a general automated process for tuning simulation inputs such that simulation output matches real world measured data. The automated process involves the following key components -- 1) Identify a model that accurately estimates the real world simulation calibration target from measured sensor data; 2) Identify …