Data

Tools

Indexes

Applications

Experiments

Open Source Science

constraint_aware_neural_networks

Code of the paper "Constraint-Aware Neural Networks for Riemann Problems"

https://github.com/jim-max/constraint_aware_neural_networks

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 8 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Code of the paper "Constraint-Aware Neural Networks for Riemann Problems"

Basic Info
  • Host: GitHub
  • Owner: jim-max
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 48.8 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

Constraint-Aware Neural Networks

Code of the paper "Constraint-Aware Neural Networks for Riemann Problems" for training constraint-aware neural networks for various model problems, see JCP or arxiv.

Installation

Using uv

Install uv, for example via pipx:

    python -m pip install --user pipx
    pipx install uv

Then run uv to set everything up:

    uv venv
    uv pip install -e .[dev]

Using pip

Install all dependencies via pip from the project directory

    pip install .[dev]

Post-Installation Steps

Setup git-lfs and pre-commit hooks:

    git lfs install  
    pre-commit install

Replication Data

The replication data is available at DaRUS, doi: 10.18419/darus-3869. Download it and put it into data/.

Usage

Run the CLI-tool:

    uv run constraint_aware_neural_networks train --help

Example:

    uv run constraint_aware_neural_networks train --model=isothermal_euler --algorithm=standard --data ./data/iso_euler_datasets/iso_euler_data_2000_n0/

Show tensorboard log:

    uv run tensorboard --logdir=./output/logs

Owner

  • Name: Jim
  • Login: jim-max
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
title: constraint_aware_neural_networks
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Jim M.
    family-names: Magiera
    email: jim.magiera@ians.uni-stuttgart.de
    affiliation: University of Stuttgart
    orcid: 'https://orcid.org/0000-0001-9807-0784'
identifiers:
  - type: doi
    value: 10.1016/j.jcp.2020.109345
    description: JCP
  - type: doi
    value: 10.48550/arXiv.1904.12794
    description: arXiv
repository-code: >-
  https://github.com/jim-max/constraint_aware_neural_networks
license: MIT

GitHub Events

Total
Last Year

Dependencies

pyproject.toml pypi
  • click *
  • matplotlib *
  • numpy *
  • pandas *
  • scikit-learn *
  • tensorboard *
  • torch *
  • tqdm *