Toward Future Energy Generation Systems: Multi-Scale Optimization with Market Interactions
Abstract not provided.
Publications
Slycat? Ensemble Analytics
Abstract not provided.
Slycat? Ensemble Analytics
Abstract not provided.
Rapid Response Data Science for COVID-19
This report describes the results of a seven day effort to assist subject matter experts address a problem related to COVID-19. In the course of this effort, we analyzed the 29K documents provided as part of the White House's call to action. This involved applying a variety of natural language processing techniques and compression-based analytics in combination with visualization techniques and assessment with subject matter experts to pursue answers to a specific question. In this paper, we will describe the algorithms, the software, the study performed, and availability of the software developed during the effort.
CAD Defeaturing using Machine Learning
Abstract not provided.
VideoSwarm: Analyzing video ensembles
IS and T International Symposium on Electronic Imaging Science and Technology
We present VideoSwarm, a system for visualizing video ensembles generated by numerical simulations. VideoSwarm is a web application, where linked views of the ensemble each represent the data using a different level of abstraction. VideoSwarm uses multidimensional scaling to reveal relationships between a set of simulations relative to a single moment in time, and to show the evolution of video similarities over a span of time. VideoSwarm is a plug-in for Slycat, a web-based visualization framework which provides a web-server, database, and Python infrastructure. The Slycat framework provides support for managing multiple users, maintains access control, and requires only a Slycat supported commodity browser (such as Firefox, Chrome, or Safari).
VideoSwarm: Analyzing Video Ensembles
Abstract not provided.
Machine Learning-based CAD Defeaturing for Tetrahedral Mesh Generation
Abstract not provided.
Defeaturing using Machine Learning
Abstract not provided.
Automated Simulation Model Preparation with Machine Learning
Abstract not provided.
VideoSwarm: Interactive Visualization of Simulation Video Ensembles
Abstract not provided.
Slycat™ User Manual
Slycat™ is a web-based system for performing data analysis and visualization of potentially large quantities of remote, high-dimensional data. Slycat™ specializes in working with ensemble data. An ensemble is a group of related data sets, which typically consists of a set of simulation runs exploring the same problem space. An ensemble can be thought of as a set of samples within a multi-variate domain, where each sample is a vector whose value defines a point in high-dimensional space. To understand and describe the underlying problem being modeled in the simulations, ensemble analysis looks for shared behaviors and common features across the group of runs. Additionally, ensemble analysis tries to quantify differences found in any members that deviate from the rest of the group. The Slycat™ system integrates data management, scalable analysis, and visualization. Results are viewed remotely on a user’s desktop via commodity web clients using a multi-tiered hierarchy of computation and data storage, as shown in Figure 1. Our goal is to operate on data as close to the source as possible, thereby reducing time and storage costs associated with data movement. Consequently, we are working to develop parallel analysis capabilities that operate on High Performance Computing (HPC) platforms, to explore approaches for reducing data size, and to implement strategies for staging computation across the Slycat™ hierarchy. Within Slycat™, data and visual analysis are organized around projects, which are shared by a project team. Project members are explicitly added, each with a designated set of permissions. Although users sign-in to access Slycat™, individual accounts are not maintained. Instead, authentication is used to determine project access. Within projects, Slycat™ models capture analysis results and enable data exploration through various visual representations. Although for scientists each simulation run is a model of real-world phenomena given certain conditions, we use the term model to refer to our modeling of the ensemble data, not the physics. Different model types often provide complementary perspectives on data features when analyzing the same data set. Each model visualizes data at several levels of abstraction, allowing the user to range from viewing the ensemble holistically to accessing numeric parameter values for a single run. Bookmarks provide a mechanism for sharing results, enabling interesting model states to be labeled and saved.
Machine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations
Abstract not provided.
Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations
Abstract not provided.
14 Results