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Neural network pruning to reduce the size of Neural Implicit Flow network.

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Neural Implicit Flow (NIF): mesh-agnostic dimensionality reduction

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  • NIF is a mesh-agnostic dimensionality reduction paradigm for parametric spatial temporal fields. For decades, dimensionality reduction (e.g., proper orthogonal decomposition, convolutional autoencoders) has been the very first step in reduced-order modeling of any large-scale spatial-temporal dynamics.

  • Unfortunately, these frameworks are either not extendable to realistic industry scenario, e.g., adaptive mesh refinement, or cannot preceed nonlinear operations without resorting to lossy interpolation on a uniform grid. Details can be found in our paper.

  • NIF is built on top of Keras, in order to minimize user's efforts in using the code and maximize the existing functionality in Keras.

Features

  • built on top of tensorflow 2 with Keras, hassle-free for many up-to-date advanced concepts and features
  • distributed learning: data parallelism across multiple GPUs on a single node
  • flexible training schedule: e.g., first Adam then fine-tunning with L-BFGS
  • performance monitoring: model weights checkpoints and restoration

Google Colab Tutorial

  1. Hello world! A simple fitting on 1D travelling wave Open In Colab

    • learn how to use class nif.NIF
    • model checkpoints/restoration
    • mixed precision training
    • L-BFGS fine tuning
  2. Tackling multi-scale data Open In Colab

    • learn how to use class nif.NIFMultiScale
    • demonstrate the effectiveness of learning high frequency data
  3. Learning linear representation Open In Colab

    • learn how to use class nif.NIFMultiScaleLastLayerParameterized
    • demonstrate on a (shortened) flow over a cylinder case from AMR solver

How to cite

If you find NIF is helpful to you, you can cite our paper in the following bibtex format

@misc{pan2022neural,
      title={Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data}, 
      author={Shaowu Pan and Steven L. Brunton and J. Nathan Kutz},
      year={2022},
      eprint={2204.03216},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License

LGPL-2.1 License

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