Training - Scientific Visualization & Data Analysis

Scientific visualization is the art of utilizing human visual cognition as a means to glean understanding and insight into scientific data, be it computed numerically or acquired by measurement.

Together with data analysis, it is a key component of today's scientific discovery process, in particular in the context of computational sciences, with application ranging from astrophysics to climatology, and from electromagnetics to mechanical engineering.​

The visualization toolkit (VTK) is an open-source, C++ software library dedicated to data visualization, image processing and computer graphics. Also offering Python and Java interfaces, it supports a broad class of visualization technique for a variety of data and attributes types.
Used extensively by thousands of users worldwide, it also constitutes the visualization engine of ParaView, an also open-source, multi-platform, application designed to visualize and analyse data on a broad set of platforms, ranging from on single processor on a laptop, to many thousands of cores on a high-performance computing cluster.

Both VTK and ParaView have achieved global recognition in the scientific visualization community over the past fifteen years. ParaView in particular has made major inroads into scientific research of today, as datasets have been growing at an extremely rapid pace and are continuing to do so.

The goal of this two-day training session is to deliver a comprehensive introduction to scientific visualization with paraView and VTK and provide a hands-on overview of the ParaView visualization application, together with a set of key VTK concepts which are necessary to a better understanding of ParaView.

This course, taught by a computational scientist and developer with over a decade worth of experience with both VTK and ParaView, is intended for an audience of computational as well as application scientists, engineers, post-docs, and students with a background or an interest in numerical simulation.

Pre-requisites

• Some understanding of scientific computing and/or modeling concepts (geometry, meshes, data attributes) is necessary. Knowledge of C++ is not required, but the Python language will be used for parts of the course.