ACENET and the Power of Scientific Visualization | Complimentary Webinar

We are excited to invite you to our upcoming complimentary community webinar!

Please join us on March 22 1:00 - 2:15 PM as ACENET introduces us to their organization and explores using supercomputing and scientific visualization to make sense of large datasets.

We all know the advantages of a bar chart instead of a table full of numbers. The bigger your dataset, the greater the value of seeing it visually and being able to pinpoint critical areas for decisions. Join us at this session to learn about powering up innovation with supercomputing, and using scientific visualization to make better decisions.

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Scientific visualization can be applied across a number of areas, including engineering, material science, sensor/drone data, geospatial, and life sciences. We all know the advantages of seeing a bar chart with data instead of reading the numbers in text. The larger your dataset, the greater the value of seeing it visually and being able to pinpoint the critical areas for decisions. This is a one-hour overview of the power of scientific visualization during the innovation process. The session begins with an explanation of supercomputing and scientific visualization. It then illustrates the decision points for a dataset size that fits on a typical desktop, before showing what can be done using a supercomputer for data that's too large for a desktop. There are no prerequisites and no technical knowledge is needed. This session is a precursor to the Interactive Data Analysis & Visualization with Paraview workshop, a 3.5 hour hands-on course where participants will learn how to run scientific visualizations on their desktop.

Key Learning Outcomes

  • To understand the role of supercomputing in scaling up innovation
  • To understand how ACENET might be able to help
  • To understand how scientific visualization is used in decision-making
  • To understand how to apply scientific visualization for effective decision-making, particularly when dealing with large and complex data sets.

Target Audience

  • Mid to senior level managers with R&D technology responsibilities
  • R&D engineering or scientific staff with no scientific visualization experience

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