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3D Scientific Visualization with Blender by Dr. Brian Kent

Recent News

NRAO and Johns Hopkins University Launch ngVLA Partnership

This partnership leverages JHU’s strengths in data science, artificial intelligence, and astrophysics, aligning with their investments in the Data Science and AI Institute and their leadership in multi-messenger astronomy. By combining the NSF NRAO’s expertise in radio astronomy with JHU’s cutting-edge computational resources, the collaboration will tackle the immense data processing and analysis challenges posed by the ngVLA and future astronomical surveys.

3D Scientific Visualization with Blender by Dr. Brian Kent

Scientific experiments have the potential to produce stunning visuals that aid in analysis as well as convey results to a broader audience.

Blender is an open source software program aimed at creating and manipulating 3D models and data. By utilizing the power of Blender, scientists can fully realize their data visualizations to produce high quality 3D renderings, animations, and video.  Any time you see an awesome science animation in the news, you will now know how to develop exciting visualizations and animations with your own data.  The book 3D Scientific Visualization with Blender takes you through an understanding of 3D graphics and modeling for different visualization scenarios in the physical sciences.  The agility of Blender and its well organized Python API make it an exciting and unique visualization suite every modern scientific/engineering workbench should include. The possibilities for generating visualizations are considerable via this ever growing software package replete with a vast community of users providing support and ideas.

Recent News

NRAO and Johns Hopkins University Launch ngVLA Partnership

This partnership leverages JHU’s strengths in data science, artificial intelligence, and astrophysics, aligning with their investments in the Data Science and AI Institute and their leadership in multi-messenger astronomy. By combining the NSF NRAO’s expertise in radio astronomy with JHU’s cutting-edge computational resources, the collaboration will tackle the immense data processing and analysis challenges posed by the ngVLA and future astronomical surveys.