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Full-Text Articles in Computational Engineering
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.
State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...
A Combined Quadtree/Delaunay Method For 2d Mesh Generation, Simon Tang
Masters Theses 1911 - February 2014
Unstructured simplicial mesh is an integral and critical part of many computational electromagnetics methods (CEM) such as the finite element method (FEM) and the boundary element method (BEM). Mesh quality and robustness have direct impact on the success of these CEM methods.
A combined quadtree/Delunay 2D mesh generator, based on the early work of Schroeder (1991, PhD), is presented. The method produces a triangulation that approximates the original geometric model but is also topologically consistent. The key advantages of the method are: (a) its robustness, (b) ability to create a-priori graded meshes, and (c) its guaranteed mesh quality.