Fire, Flow and Flight

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Date/Time: 29 November 2017, 09:00am - 10:45am
Venue: Amber 2
Location: Bangkok Int'l Trade & Exhibition Centre (BITEC)
Session Chair: Nobuyuki Umetani, Autodesk Research, Canada

Interactive Wood Combustion for Botanical Tree Models

Summary: We present a novel method for the combustion of botanical tree models. Tree models are represented as particles that store biological and physical attributes that drive the kinetic behavior of a plant and the exothermic reaction of the combustion, which is efficiently processed in real-time.

Author(s): Soeren Pirk, Stanford Univeristy
Michal Jarzabek, Adam Mickiewicz University
Torsten Hädrich, None (freelancer)
Dominik Michels, King Abdullah University Of Science And Technology (KAUST)
Wojciech Palubicki, Adam Mickiewicz University

Speaker(s): Soren Pirk, Stanford University

How to Train Your Dragon: Example-Guided Control of Flapping Flight

Summary: We present a control method for flying creatures, which are aerodynamically simulated, interactively controllable, and equipped with a variety of motor skills such as soaring, gliding, hovering, and diving. Each motor skill is represented as Deep Neural Networks (DNN) and learned using Deep Q-Learning (DQL).

Author(s): Jungdam Won, Seoul National University
Jongho Park, Seoul National University
Kwanyu Kim, Seoul National University
Jehee Lee, Seoul National University

Speaker(s): Jungdam Won, Seoul National University

A Hyperbolic Geometric Flow for Evolving Films and Foams

Summary: A reformation of soap film dynamics as a hyperbolic geometric flow. Our model provides a fast simulation that elegantly leads films to the steady states as Plateau’s laws describe.

Author(s): Sadashige ISHIDA, The University of Tokyo, Nikon Corporation
Masafumi YAMAMOTO, The University of Tokyo
Ryoichi ANDO, National Institute of Informatics
Toshiya HACHISUKA, The University of Tokyo

Speaker(s): Sadashige Ishida, Nikon Corporation, and The University of Tokyo

City-Scale Traffic Animation Using Statistical Learning and Metamodel-Based Optimization

Summary: This paper demonstrates a framework that enables city-scale traffic visualization and animation through statistical learning and metamodel-based optimization.

Author(s): Weizi Li, University of North Carolina at Chapel Hill
David Wolinski, University of North Carolina at Chapel Hill
Ming Lin, University of North Carolina at Chapel Hill

Speaker(s): Weizi Li, University of North Carolina at Chapel Hill