SPNets: 深層ニューラルネットワークのための微分可能な流体力学
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
粒子ベースの流体シミュレーションをニューラルネットワークの層として実装し、流体パラメータをデータから学習したり液体操作の制御・方策学習を可能にした研究。
著者: Connor Schenck, Dieter Fox
分類: cs.RO
原文アブストラクト
In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented as a neural network, the resulting fluid dynamics are fully differentiable. We then show how this can be successfully used to learn fluid parameters from data, perform liquid control tasks, and learn policies to manipulate liquids.
