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Andrea Brambilla
Researcher at University of Bergen
Publications - 11
Citations - 199
Andrea Brambilla is an academic researcher from University of Bergen. The author has contributed to research in topics: Visualization & Scientific visualization. The author has an hindex of 6, co-authored 11 publications receiving 175 citations.
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Proceedings ArticleDOI
Illustrative Flow Visualization: State of the Art, Trends and Challenges
TL;DR: An overview of the existing illustrative techniques for flow visualization is given, which problems have been solved and which issues still need further investigation are highlighted, and remarks and insights on the current trends in illustrative flow visualization are provided.
Journal ArticleDOI
3D risk management for hydrogen installations
Trygve Skjold,D. Siccama,Helene Hisken,Andrea Brambilla,Prankul Middha,Katrina M. Groth,Angela Christine LaFleur +6 more
TL;DR: The paper outlines the background for 3DRM and presents a proof-of-concept risk assessment for a hypothetical hydrogen filling station, which focuses on dispersion, fire and explosion scenarios resulting from loss of containment of gaseous hydrogen.
Journal ArticleDOI
Illustrative Membrane Clipping
TL;DR: This paper presents a new technique, which combines the simple clipping interaction with automated selective feature preservation using an elastic membrane, and demonstrates that this method can act as a flexible and non‐invasive replacement of traditional clipping planes.
Journal ArticleDOI
Composite Flow Maps
Daniel Cornel,Artem Konev,B. Sadransky,Zsolt Horváth,Andrea Brambilla,Ivan Viola,Jürgen Waser +6 more
TL;DR: This paper combines flows as individual ribbons in one composite flow map that can handle an arbitrary number of sources and sinks, and describes a method for auto‐deriving zones from geospatial data according to application requirements.
Journal ArticleDOI
Fast Blending Scheme for Molecular Surface Representation
Julius Parulek,Andrea Brambilla +1 more
TL;DR: A novel surface representation is introduced that resembles the SES and approaches the rendering performance of the Gaussian model and a GPU-based ray-casting algorithm is proposed that efficiently visualize this molecular representation.