A teaching paper demonstrates perceptron training, PINNs, autoencoders, and SINDy on pendulum examples, with code notebooks provided online.
PhyCV: The First Physics-inspired Computer Vision Library
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the current release emulate, in a metaphoric sense, the propagation of light through a physical medium with natural and engineered diffractive properties followed by coherent detection. Unlike traditional algorithms that are a sequence of hand-crafted empirical rules or deep learning algorithms that are usually data-driven and computationally heavy, physics-inspired algorithms leverage physical laws of nature as blueprints for inventing algorithms. PhyCV features low-dimensionality and high- efficiency, making it ideal for edge computing applications. We demonstrate real-time video processing on NVIDIA Jetson Nano using PhyCV. In addition, these algorithms have the potential to be implemented in real physical devices for fast and efficient computation in the form of analog computing. The open-sourced code is available at https://github.com/JalaliLabUCLA/phycv
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physics.ed-ph 1years
2025 1verdicts
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An introduction to Neural Networks for Physicists
A teaching paper demonstrates perceptron training, PINNs, autoencoders, and SINDy on pendulum examples, with code notebooks provided online.