Learning Physical Constraints with Neural Projections

23 Jun 2020Shuqi YangXingzhe HeBo Zhu

We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints. A neural projection operator liesat the heart of our approach, composed of a lightweight network with an embedded recursive architecture that interactively enforces learned underpinning constraints and predicts the various governed behaviors of different physical systems... (read more)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet