Loading repository data…
nasa / repository
This is the code for "Neural Network Reconstruction of Plasma Space-Time" by C.Bard and J.Dorelli (DOI: 10.3389/fspas.2021.732275). It is a Physics-Informed Transformer Neural Network which was used to reconstruct one-dimensional (M)HD shocktubes from partial samples. Includes source code, data, and jupyter notebooks for scientific reproduction
This code is associated with the paper "Neural Network Reconstruction of Plasma Space-Time" by C.Bard and J. Dorelli (DOI: 10.3389/fspas.2021.732275)
Jupyter notebooks for reproducing paper plots are found in ./reproduce_plots ; the main programs are found in run_euler.py and run_mhd_recon.py.
The 'model' folders contain the best trained weights for each network. The 'spacetime' folders contain the raw HDF5 data from the baseline simulations.
Package Requirements: tensorflow, numpy, scipy, pylab, h5py
If you would like to cite this paper, the BibTex format is:
@ARTICLE{2021FrASS...8..146B,
author = {{Bard}, C. and {Dorelli}, J.~C.},
title = "{Neural Network Reconstruction of Plasma Space-Time}",
journal = {Frontiers in Astronomy and Space Sciences},
keywords = {Space Physics, reconstruction, Physics-informed neural network, MHD, computational methods},
year = 2021,
month = sep,
volume = {8},
eid = {146},
pages = {146},
doi = {10.3389/fspas.2021.732275},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021FrASS...8..146B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Selected from shared topics, language and repository description—not editorial ratings.
gordicaleksa /
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.
weisscharlesj /
Scientific Computing for Chemists with Python is a free book for teaching basic coding skills to chemists using Python, Jupyter notebooks, and the other Python software. This textbook teaches a variety of Python packages including NumPy, SciPy, matplotlib, pandas, seaborn, nmrglue, SymPy, scikit-image, scikit-learn, and others.
vbartle /
This is a companion to the ‘Mathematical Foundations’ section of the book, Mathematics for Machine Learning by Marc Deisenroth, Aldo Faisal and Cheng Ong, written in python for Jupyter Notebook.
ChristianFJung /
:thought_balloon: This is the accompanying repo for my article on converting a Jupyter Notebook to a streamlit web app.
Olow304 /
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.