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Found 6 papers, 1 papers with code
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks.
Robotics
Reasoning About Liquids via Closed-Loop Simulation
In this paper, we show how to close the loop between liquid simulation and real-time perception.
See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content
Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher.
Visual Closed-Loop Control for Pouring Liquids
We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container.
Towards Learning to Perceive and Reason About Liquids
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning.
Detection and Tracking of Liquids with Fully Convolutional Networks
In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids.
