Abstract
This suite of code learns neural network models to emulate the dynamics of time series data.
To do so, it learns a set of "neural shape functions" that provide a common vocabulary for expressing the various dynamics operators.
The code is implemented in Python and TensorFlow.
- Developers:
-
Desautels, Thomas [1] ; Reeves, Majerle [2]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- UC Merced
- Release Date:
- 2019-08-09
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Version:
- 0.0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 31120
- Site Accession Number:
- 989386
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Desautels, Thomas, and Reeves, Majerle.
Modeling Time Series Data with Ordinary Differential Equations and Neural Networks.
Computer Software.
https://github.com/tadesautels/PSINN.
USDOE National Nuclear Security Administration (NNSA).
09 Aug. 2019.
Web.
doi:10.11578/dc.20191011.2.
Desautels, Thomas, & Reeves, Majerle.
(2019, August 09).
Modeling Time Series Data with Ordinary Differential Equations and Neural Networks.
[Computer software].
https://github.com/tadesautels/PSINN.
https://doi.org/10.11578/dc.20191011.2.
Desautels, Thomas, and Reeves, Majerle.
"Modeling Time Series Data with Ordinary Differential Equations and Neural Networks." Computer software.
August 09, 2019.
https://github.com/tadesautels/PSINN.
https://doi.org/10.11578/dc.20191011.2.
@misc{
doecode_31120,
title = {Modeling Time Series Data with Ordinary Differential Equations and Neural Networks},
author = {Desautels, Thomas and Reeves, Majerle},
abstractNote = {This suite of code learns neural network models to emulate the dynamics of time series data.
To do so, it learns a set of "neural shape functions" that provide a common vocabulary for expressing the various dynamics operators.
The code is implemented in Python and TensorFlow.},
doi = {10.11578/dc.20191011.2},
url = {https://doi.org/10.11578/dc.20191011.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20191011.2}},
year = {2019},
month = {aug}
}