Create Neural Ordinary Differential Equations with 'tensorflow'
Overview
Provides a framework for the creation and use of Neural ordinary differential equations with the 'tensorflow' and 'keras' packages. The idea of Neural ordinary differential equations comes from Chen et al. (2018) doi:10.48550/arXiv.1806.07366, and presents a novel way of learning and solving differential systems.
- Initial CRAN submission.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 7 (4 internal)
- Examples that run
- 100%3 run in a plain check (3 of them under a condition)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- tfNeuralODE-Adjoint HTML
- tfNeuralODE-Spirals HTML
Downloads
Repository
Stars over time
9 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/semran9/tfneuralode on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2023-10-17 · The repository has a site deploy workflow
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.0Latest2023-10-16 · current release
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-10-16
- Total releases
- 1 / 3 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 440 KB
Cite
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