references.bib

@article{palmroth2018,
  doi = {10.1007/s41115-018-0003-2},
  url = {https://doi.org/10.1007/s41115-018-0003-2},
  year = {2018},
  month = aug,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {4},
  number = {1},
  author = {Minna Palmroth and Urs Ganse and Yann Pfau-Kempf and Markus Battarbee and Lucile Turc and Thiago Brito and Maxime Grandin and Sanni Hoilijoki and Arto Sandroos and Sebastian von Alfthan},
  title = {Vlasov methods in space physics and astrophysics},
  journal = {Living Reviews in Computational Astrophysics}
}

@InProceedings{park2019,
author = {Park, Jeong Joon and Florence, Peter and Straub, Julian and Newcombe, Richard and Lovegrove, Steven},
title = {DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}


@article{2020fourier,
      title={Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains}, 
      author={Matthew Tancik and Pratul P. Srinivasan and Ben Mildenhall and Sara Fridovich-Keil and Nithin Raghavan and Utkarsh Singhal and Ravi Ramamoorthi and Jonathan T. Barron and Ren Ng},
      year={2020},
      eprint={2006.10739},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@software{zfp,
author = {Lindstrom, Peter},
month = nov,
title = {{Fixed-Rate Compressed Floating-Point Arrays}},
version = {develop},
year = {2014}
}

@book{gmm,
author = {Bishop, Christopher M.},
title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
year = {2006},
isbn = {0387310738},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg}
}

@article{misner_2004,
doi = {10.1088/0264-9381/21/3/014},
url = {https://dx.doi.org/10.1088/0264-9381/21/3/014},
year = {2004},
month = {jan},
publisher = {},
volume = {21},
number = {3},
pages = {S243},
author = {Charles W Misner},
title = {Spherical harmonic decomposition on a cubic grid},
journal = {Classical and Quantum Gravity},
abstract = {A method is described by which a function defined on a cubic grid (as from a finite difference solution of a partial differential equation) can be resolved into spherical harmonic components at some fixed radius. This has applications to the treatment of boundary conditions imposed at radii larger than the size of the grid, following Abrahams et al (1998 Phys. Rev. Lett. 80 1812–5). In the method described here, the interpolation of the grid data to the integration 2-sphere is combined in the same step as the integration to extract the spherical harmonic amplitudes, which become sums over grid points. Coordinates adapted to the integration sphere are not needed.}
}

@article{vinas_gurgiolo_2009,
author = {Viñas, Adolfo F. and Gurgiolo, Chris},
title = {Spherical harmonic analysis of particle velocity distribution function: Comparison of moments and anisotropies using Cluster data},
journal = {Journal of Geophysical Research: Space Physics},
volume = {114},
number = {A1},
pages = {},
keywords = {spherical harmonics, plasma velocity distribution function},
doi = {https://doi.org/10.1029/2008JA013633},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2008JA013633},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2008JA013633},
abstract = {This paper presents a spherical harmonic analysis of the plasma velocity distribution function using high-angular, energy, and time resolution Cluster data obtained from the PEACE spectrometer instrument to demonstrate how this analysis models the particle distribution function and its moments and anisotropies. The results show that spherical harmonic analysis produced a robust physical representation model of the velocity distribution function, resolving the main features of the measured distributions. From the spherical harmonic analysis, a minimum set of nine spectral coefficients was obtained from which the moment (up to the heat flux), anisotropy, and asymmetry calculations of the velocity distribution function were obtained. The spherical harmonic method provides a potentially effective “compression” technique that can be easily carried out onboard a spacecraft to determine the moments and anisotropies of the particle velocity distribution function for any species. These calculations were implemented using three different approaches, namely, the standard traditional integration, the spherical harmonic (SPH) spectral coefficients integration, and the singular value decomposition (SVD) on the spherical harmonic methods. A comparison among the various methods shows that both SPH and SVD approaches provide remarkable agreement with the standard moment integration method.},
year = {2009}
}

@article{lee_2019,
doi = {10.21105/joss.01237},
url = {https://doi.org/10.21105/joss.01237},
year = {2019},
publisher = {The Open Journal},
volume = {4},
number = {36},
pages = {1237},
author = {Gregory R. Lee and Ralf Gommers and Filip Waselewski and Kai Wohlfahrt and Aaron O’Leary},
title = {PyWavelets: A Python package for wavelet analysis},
journal = {Journal of Open Source Software}
}