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Abstract: Fluid phenomena are ubiquitous to our world experience: winds swooshingthrough trembling leaves, turbulent water streams running down a river,and cellular patterns generated from wrinkled flames are some few examples. These complex phenomena capture our attention and awe due to the beautifully materialized complex patterns and become crucial elements to artistically support storytelling. In virtual environments, however, sophisticated manipulation of animated flow structures is stilla burdensome task. Given the amount of available fluid simulation data,data-driven approaches have emerged as attractive solutions. In this talk, I will present our recent works on data-driven methods for art-directable fluid simulations.

Short bio: Byungsoo Kim recently completed his joint PhD at Computer Graphics Lab at ETH Zurich and Disney Research Studios, where Prof. Markus Gross has advised him. His research mainly focuses on deep learning methods for art-directable fluid simulations. Prior to that, he received a BSc in Computer Science from KAIST in 2009 and an MSc in Computer Science from ETH Zurich in 2016 after 4 years of working as a research engineer in Graphics industry. (Website: www.byungsoo.me)