YangyangFu/neuromancer

Pytorch-based framework for solving parametric constrained optimization problems.

NeuroMANCER

Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations

Complete Documentation

Setup

Clone and install neuromancer, linear maps, and emulator submodules

user@machine:~$ mkdir ecosystem; cd ecosystem
user@machine:~$ git clone https://github.com/pnnl/neuromancer.git
user@machine:~$ git clone https://github.com/pnnl/psl.git
user@machine:~$ git clone https://github.com/pnnl/slim.git

# Resulting file structure:
    ecosystem/
        neuromancer/
        psl/
        slim/

Create the environment via .yml (Linux)

user@machine:~$ conda env create -f env.yml
(neuromancer) user@machine:~$ source activate neuromancer

If .yml env creation fails create the environment manually

user@machine:~$ conda config --add channels conda-forge pytorch
user@machine:~$ conda creatcd e -n neuromancer python=3.7
user@machine:~$ source activate neuromancer
(neuromancer) user@machine:~$ conda install pytorch torchvision -c pytorch
(neuromancer) user@machine:~$ conda install scipy pandas matplotlib control pyts numba scikit-learn dill
(neuromancer) user@machine:~$ conda install mlflow boto3
(neuromancer) user@machine:~$ conda install -c powerai gym
(neuromancer) user@machine:~$ conda install pytest hypothesis
(neuromancer) user@machine:~$ conda install -c coecms celluloid

Install neuromancer ecosystem

(neuromancer) user@machine:~$ cd psl
(neuromancer) user@machine:~$ python setup.py develop
(neuromancer) user@machine:~$ cd ../slim
(neuromancer) user@machine:~$ python setup.py develop
(neuromancer) user@machine:~$ cd ../ # into neuromancer
(neuromancer) user@machine:~$ python setup.py develop

Examples

Several tutorials and examples using Neuromancer to solve different parameteric programming problems can be found in the examples folder.

Publications

• Drgoňa, J., Tuor, A. R., Chandan, V., & Vrabie, D. L. (2021). Physics-constrained deep learning of multi-zone building thermal dynamics. Energy and Buildings, 243, 110992.
• Tuor, A., Drgona, J., & Vrabie, D. (2020). Constrained neural ordinary differential equations with stability guarantees. arXiv preprint arXiv:2004.10883.
• Drgona, Jan, et al. "Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning." arXiv preprint arXiv:2011.03699 (2020).
• E. Skomski, S. Vasisht, C. Wight, A. Tuor, J. Drgoňa and D. Vrabie, "Constrained Block Nonlinear Neural Dynamical Models," 2021 American Control Conference (ACC), 2021, pp. 3993-4000, doi: 10.23919/ACC50511.2021.9482930.
• Skomski, E., Drgoňa, J., & Tuor, A. (2021, May). Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. In Learning for Dynamics and Control (pp. 980-991). PMLR.
• Drgoňa, J., Tuor, A., Skomski, E., Vasisht, S., & Vrabie, D. (2021). Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. IFAC-PapersOnLine, 54(6), 14-19.
• Drgona, J., Skomski, E., Vasisht, S., Tuor, A., & Vrabie, D. (2020). Spectral Analysis and Stability of Deep Neural Dynamics. arXiv preprint arXiv:2011.13492.
• Drgona, J., Tuor, A., & Vrabie, D. (2020). Constrained physics-informed deep learning for stable system identification and control of unknown linear systems. arXiv preprint arXiv:2004.11184.

Cite as

@article{Neuromancer2022,
  title={{NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}},
  author={Tuor, Aaron and Drgona, Jan and Skomski, Mia},
  Url= {https://github.com/pnnl/neuromancer}, 
  year={2022}
}