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Full-Text Articles in Physical Sciences and Mathematics
Sequential Optimization For Stressor-Informed Test Planning Through Integration Of Experimental And Simulated Data, Jacob Brecheisen
Sequential Optimization For Stressor-Informed Test Planning Through Integration Of Experimental And Simulated Data, Jacob Brecheisen
Data Science Undergraduate Honors Theses
This technical report details an innovative approach in reliability engineering aimed at maximizing system durability through a synergistic use of physical experimentation and computer-based modeling. Our methodology explores the efficient design and analysis of computer experiments and physical tests to facilitate accelerated reliability growth, while leveraging a sequential integration of data from these two distinct sources: costly physical experiments, characterized by random errors, and inexpensive computer simulations, marked by inherent systematic errors. The key innovation lies in the adoption of a closed-loop design and analysis method. This method begins by identifying a viable subset of important environmental stressors—such as temperature, …
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Electronic Theses and Dissertations
In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …