GAMES Webinar 2021 – 181期(仿真模拟专题) | Pengbin Tang (Université de Montréal),杜韬 (MIT CSAIL)

【GAMES Webinar 2021-181期】(仿真模拟专题)

报告嘉宾1:Pengbin Tang (Université de Montréal)


报告题目:A Harmonic Balance Approach for Designing Compliant Mechanical Systems with Nonlinear Periodic Motions


We present a computational method for designing compliant mechanical systems that exhibit large-amplitude oscillations. The technical core of our approach is an optimization-driven design tool that combines sensitivity analysis for optimization with the Harmonic Balance Method for simulation. By establishing dynamic force equilibrium in the frequency domain, our formulation avoids the major limitations of existing alternatives: it handles nonlinear forces, side-steps any transient process, and automatically produces periodic solutions. We introduce design objectives for amplitude optimization and trajectory matching that enable intuitive high-level authoring of large-amplitude motions. Our method can be applied to many types of mechanical systems, which we demonstrate through a set of examples involving compliant mechanisms, flexible rod networks, elastic thin shell models, and multi-material solids. We further validate our approach by manufacturing and evaluating several physical prototypes.


I am a PhD student at LIGUM at Université de Montréal under supervision of Prof. Bernhard Thomaszewski. Before that, I obtained my Bachelor’s and Master’s at Shanghai Film Academy of Shanghai University, P.R.China, under supervision of Prof. Youdong Ding and Prof. Dongjin Huang. My research interests are computer graphics, numerical simulation and optimization, computational design, as well as physics-based animation.


报告嘉宾2:杜韬 (MIT CSAIL)


报告题目:Functional Optimization of Fluidic Devices with Differentiable Stokes Flow


We present a method for performance-driven optimization of fluidic devices. In our approach, engineers provide a high-level specification of a device using parametric surfaces for the fluid-solid boundaries. They also specify desired flow properties for inlets and outlets of the device. Our computational approach optimizes the boundary of the fluidic device such that its steady-state flow matches desired flow at outlets. In order to deal with computational challenges of this task, we propose an efficient, differentiable Stokes flow solver. Our solver provides explicit access to gradients of performance metrics with respect to the parametric boundary representation. This key feature allows us to couple the solver with efficient gradient-based optimization methods. We demonstrate the efficacy of this approach on designs of five complex 3D fluidic systems. Our approach makes an important step towards practical computational design tools for high-performance fluidic devices.


Tao Du is a final-year Ph.D. student in Computer Science at MIT Computer Science & Artificial Intelligence Laboratory (CSAIL), advised by Professor Wojciech Matusik. His research lies at the intersection of computer graphics, robotics, and machine learning, with a broad interest in developing computational methods for solving challenging design and control problems in complex, real-world physics systems. His work has been published at top-tier computer graphics, robotics, and machine learning journals and conferences and has been covered by multiple media outlets.





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