Development of algorithms and implementation strategies for visualizing simulation data produced as part of the uncertainty quantification (UQ), and.Investigation of mathematical and computational techniques that would help bridge the simulation-to-visualization gap when one is dealing with stochastic simulation data – in particular data generated through the generalized Polynomial Chaos method.Saquib has been an active participant for the second two years of the project.Īs a collaborative project, we have attempted to maintain three different goals throughout the life of the project: His research focuses on problems in uncertainty quantification, visualization, and generalized polynomial chaos (gPC) based methods for stochastic PDE simulations in different application areas, such as biophysics and fluid dynamics. Nazmus Saquib is currently an MS student in the Computational Engineering and Science (CES) Program at the University of Utah. Chao was an active participant during the first two years of the project. His research interest is Uncertainty Visualization and Computer Graphics. His research focus is on Stochastic computations and uncertainty quantification, Design and optimization under uncertainty, Data assimilation, High-order numerical methods, Modeling and simulation of complex systems.Ĭhao Yang is currently a PhD student in School of Computing at University of Utah. Xiu is currently a Professor in the Department of Mathematics at the University of Utah, and is a member of the Scientific Computing and Imaging Institute. His research focus is on large-scale scientific computing and visualization, with an emphasis on the scientific cycle of mathematical modeling, computation, visualization, evaluation, and understanding.ĭr. Kirby is currently an Associate Professor in the School of Computing and a faculty member of the Scientific Computing and Imaging Institute at University of Utah. This is accomplished by developing strategies and techniques for augmenting current visualization techniques used for visualizing spatio-temporal fields with UQ information in a seamless way.ĭr. This research addresses the questions of how does one accurately and efficiently post-process stochastic simulation fields and how does one effectively and succinctly convey the results. Visualization is the window through which scientists examine their data for deriving new science, and hence visualization methods which depict underlying uncertainty information are crucial. In addition to the traditional components of the pipeline, there has been a recent surge of interest in uncertainty quantification (UQ). In this age of scientific computing, the simulation science pipeline of mathematical modeling, simulation and evaluation is a commonly employed rendition of the scientific method. IIS-0914564 (Kirby) and IIS-0914447 (Xiu).Īny opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.ĪF: Small: Collaborative Research: Analysis and Visualization of Stochastic Simulation Solutions This material is based upon collaborative work supported by the National Science Foundation under Grant No. Analysis and Visualization of Stochastic Simulation Solutions
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