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Full-Text Articles in Applied Mathematics
Gmres Convergence Of Block Preconditioners For Nonsymmetric Matrices, Miguel A. Mascorro
Gmres Convergence Of Block Preconditioners For Nonsymmetric Matrices, Miguel A. Mascorro
Theses and Dissertations
GMRES is an iterative method for solving linear systems that minimizes the residual over the k-dimensional Krylov subspace at iteration k. Murphy, Golub and Wathen in [11] show that saddle point type matrices can be preconditioned so that GMRES converges in two or three steps. Ipsen in [10] extends this work to matrixes where the (2,2) block is nonzero. However, the three step convergence result no longer holds in this case. In this thesis we investigate how many more steps are needed for convergence as a function of the size of that (2,2) block.
Could Cultures Determine The Course Of Epidemics And Explain Waves Of Covid-19?, Md Salman Rahman
Could Cultures Determine The Course Of Epidemics And Explain Waves Of Covid-19?, Md Salman Rahman
Theses and Dissertations
Coronavirus Disease (COVID-19), caused by the SARS-CoV-2 virus, is an infectious disease that quickly became a pandemic spreading with different patterns in each country. Travel bans, lockdowns, social distancing, and non-essential business closures caused significant economic disruptions and stalled growth worldwide in the pandemic’s first year. In almost every country, public health officials forced and/or encouraged Nonpharmaceutical Interventions (NPIs) such as contact tracing, social distancing, masks, and quarantine. Human behavioral decision-making regarding social isolation significantly impedes global success in containing the pandemic. This thesis focuses on human behaviors and cultures related to the decision-making of social isolation during the pandemic. …
Mathematical And Statistical Modeling With Deep Neural Networks, Albert Romero
Mathematical And Statistical Modeling With Deep Neural Networks, Albert Romero
Theses and Dissertations
General adversarial networks (GANs) are a form of machine learning that includes two neural networks competing in a zero-sum game. One network produces artificial, while the other tries to distinguish artificial data from real. The Wasserstein general adversarial network with gradient penalty (WGAN-GP) variant of this technique is used to produce solutions for ordinary and partial differential equations.