GAMES Webinar 2021 – 211期(模拟专题) | Mengyu Chu (Max Planck Institute for Informatics)，Pingchuan Ma（MIT CSAIL）
【GAMES Webinar 2021-211期】(模拟专题)
报告嘉宾1：Mengyu Chu (Max Planck Institute for Informatics)
报告题目：Learning Meaningful Controls for Fluids
While modern fluid simulation methods achieve high-quality simulation results, it is still a big challenge to interpret and control motion from visual quantities, such as the advected marker density. These visual quantities play an important role in user interactions: Being familiar and meaningful to humans, these quantities have a strong correlation with the underlying motion. We propose a novel data-driven conditional adversarial model that solves the challenging, and theoretically ill-posed problem of deriving plausible velocity fields from a single frame of a density field. Besides density modifications, our generative model enables the control of the results using all of the following control modalities: obstacles, physical parameters, kinetic energy, and vorticity. We show the high quality and versatile controllability of our results for density-based inference, realistic obstacle interaction, and sensitive responses to modifications of physical parameters, kinetic energy, and vorticity.
Currently, Mengyu Chu is a Lise Meitner Postdoctoral Research Fellow at Max Planck Institute for Informatics. Previously, she studied as a Ph.D. student at Technical University of Munich, from 2014 to 2020. Her research focuses on the combination of deep-learning-based algorithms and physical simulations. The research goal is to improve the flexibility of simulations and to enhance the accuracy and generalizability of deep learning algorithms.
报告嘉宾2：Pingchuan Ma（MIT CSAIL）
报告题目：DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer’s geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer’s performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.
Pingchuan Ma is a third-year Ph.D. student at MIT CSAIL advised by Prof. Wojciech Matusik. His research mainly focuses on computer graphics and machine learning.
许威威现任浙江大学CAD&CG国家重点实验室百人计划研究员。曾任日本立命馆大学博士后，微软亚洲研究院网络图形组研究员, 杭州师范大学浙江省钱江学者特聘教授。主要研究方向为计算机图形学、三维重建、深度学习、物理仿真及3D打印。在国内外高水平学术会议和期刊发表论文80余篇，其中ACM Transactions on Graphics, IEEE TVCG, 及IEEE CVPR等CCF-A类论文20余篇。获中国和美国授权专利15项。所开发的三维注册和重建技术在高精度扫描仪及人体三维重建系统中得到应用。2014年受国家自然科学基金优秀青年基金资助，主持国家自然科学基金重点项目一项，获浙江省自然科学二等奖一项。
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