Distilling Governing Laws and Source Input for Dynamical Systems from Videos. (arXiv:2205.01314v1 [cs.CV])

Distilling interpretable physical laws from videos has led to expanded
interest in the computer vision community recently thanks to the advances in
deep learning, but still remains a great challenge. This paper introduces an
end-to-end unsupervised deep learning framework to uncover the explicit
governing equations of dynamics presented by moving object(s), based on
recorded videos. Instead in the pixel (spatial) coordinate system of image
space, the physical law is modeled in a regressed underlying physical
coordinate system where the physical …

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