pnif_old
Neural network pruning to reduce the size of Neural Implicit Flow network.
Science Score: 64.0%
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✓CITATION.cff file
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✓codemeta.json file
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2 of 3 committers (66.7%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary
Keywords
Repository
Neural network pruning to reduce the size of Neural Implicit Flow network.
Basic Info
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Neural Implicit Flow (NIF): mesh-agnostic dimensionality reduction
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
Hello world! A simple fitting on 1D travelling wave
- learn how to use class
nif.NIF - model checkpoints/restoration
- mixed precision training
- L-BFGS fine tuning
- learn how to use class
Tackling multi-scale data
- learn how to use class `nif.NIFMultiScale`
- demonstrate the effectiveness of learning high frequency data
- Learning linear representation
- learn how to use class
nif.NIFMultiScaleLastLayerParameterized - demonstrate on a (shortened) flow over a cylinder case from AMR solver
- learn how to use class
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
Owner
- Name: Olivia T. Zahn
- Login: oliviatessa
- Kind: user
- Location: Seattle
- Company: University of Washington
- Repositories: 2
- Profile: https://github.com/oliviatessa
Physics Ph.D. candidate in the Kutz Research Group at the University of Washington. Focus: deep learning, model order reduction, and bio-inspired DL models.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Neural Implicit Flow: a mesh-agnostic
dimensionality reduction paradigm of
spatio-temporal data
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Shaowu
family-names: Pan
email: shawnpan@uw.edu
affiliation: 'University of Washington, Seattle'
orcid: 'https://orcid.org/0000-0002-2462-362X'
- given-names: Steven
family-names: Brunton
email: sbrunton@uw.edu
affiliation: 'University of Washington, Seattle'
- given-names: J. Nathan
family-names: Kutz
email: kutz@uw.edu
affiliation: 'University of Washington, Seattle'
identifiers:
- type: doi
value: ???
repository-code: 'https://github.com/pswpswpsw/nif'
url: '???'
license: LGPL-2.1
GitHub Events
Total
Last Year
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| shaowu | s****n@u****u | 65 |
| olivia | o****s@u****u | 22 |
| Shaowu Pan | p****w | 1 |
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Last synced: about 2 years ago
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Dependencies
- matplotlib *
- numpy *
- x.strip *