jaxfss
JAX/Flax implementation of finite-size scaling analysis
Installation
jaxfss can be installed with pip with the following command:
pip install jaxfss
Quickstart
Check out the documentation!!
Other packages
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Finite-size scaling package by Gaussian process with C++
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Finite-size scaling package by neural network and Gaussian process with Python (PyTorch)
Citation
Please cite this paper when you use this package for your research!!
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[Full paper] Ryosuke Yoneda and Kenji Harada, Neural Network Approach to Scaling Analysis of Critical Phenomena, arXiv: 2209.01777.
@article{yoneda2022neural, title={Neural Network Approach to Scaling Analysis of Critical Phenomena}, author={Yoneda, Ryosuke and Harada, Kenji}, url={https://arxiv.org/abs/2209.01777}, journal={arXiv preprint arXiv:2209.01777}, year={2022} }
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[Conference paper] Currently preparing!!
Release files for jaxfss 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jaxfss-0.1.0.tar.gz | 4.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfss-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.6 kB
Release files / jaxfss-0.1.0.tar.gz
| Download URL | jaxfss-0.1.0.tar.gz |
|---|---|
| Size | 4.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.9.14
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Release files / jaxfss-0.1.0-py3-none-any.whl
| Download URL | jaxfss-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.14
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