Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/IdahoLabResearch/BIhNNs

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).
https://github.com/IdahoLabResearch/BIhNNs

bayesian-inference hamiltonian-monte-carlo langevin-dynamics neural-networks no-u-turn-sampler physics-informed-neural-networks sampling-methods

Last synced: 14 days ago
JSON representation

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

Awesome Lists containing this project

README 

        
Copyright (c) 2022 Battelle Energy Alliance, LLC



Licensed under MIT License, please see LICENSE for details



https://github.com/IdahoLabResearch/BIhNNs/blob/main/LICENSE



# BIhNNs



## BIhNNs: Bayesian Inference with (Hamiltonian and other) Neural Networks



* Train neural network architectures like deep neural nets (DNN), Neural ODEs, Hamiltonian neural nets (HNNs), and symplectic neural nets to learn probability distribution spaces.

* Use the trained neural net to perform sampling without requiring gradient information of the target probability density.

* State-of-the-art sampling schemes like Langevin Monte Carlo, Hamiltonian Monte Carlo, and No-U-Turn Sampling are available for use with the above-mentioned trained neural nets.



# Publications



The code in this repository is part of the following two papers available on arXiv:



* Dhulipala et al. (2022) Bayesian Inference with Latent Hamiltonian Neural Networks. https://arxiv.org/abs/2208.06120.

* Dhulipala et al. (2022) Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference. https://arxiv.org/abs/2209.09349.



The below figure presents the workflow for performing sampling with (Hamiltonian and other) neural networks.



![Figure](Schematic.png)



# Using the code



## Deep neural nets (DNNs) [literature: https://arxiv.org/abs/1906.01563]



* go to src/dnns/

* Include the Hamiltonian of the required probability distribution in the `functions.py` file. Some example probability distributions are already included. For information on the Hamiltonian of a probability distribution, see https://arxiv.org/pdf/1206.1901.pdf%20http://arxiv.org/abs/1206.1901.pdf.

* Adjust the parameters in `get_args.py`

* Run `train_dnn.py` to train the DNN model. The training data will be stored in a pkl file with the name the user specified in `get_args.py`. The trained DNN will be stored in a tar file with the name the user specified in `get_args.py`.

* Then run, either `dnn_lmc.py`, `dnn_hmc.py`, `dnn_nuts_online.py` to, respectively, perform Langevin Monte Carlo, Hamiltonian Monte Carlo, and No-U-Turn Sampling with the trained DNN. Note that the user specified sampling parameters can be adjusted in these files.

* For No-U-Turn Sampling, an online error monitoring scheme as described in (https://arxiv.org/abs/2208.06120) is used. To turn this feature off, set the `hnn_threshold` parameter in `dnn_nuts_online.py` to a large value like 1000.



## Hamiltonian neural nets (HNNs) [literature: https://arxiv.org/abs/1906.01563]



* go to src/hnns/

* Include the Hamiltonian of the required probability distribution in the `functions.py` file. Some example probability distributions are already included. For information on the Hamiltonian of a probability distribution, see https://arxiv.org/pdf/1206.1901.pdf%20http://arxiv.org/abs/1206.1901.pdf.

* Adjust the parameters in `get_args.py`

* Run `train_hnn.py` to train the HNN model. The training data will be stored in a pkl file with the name the user specified in `get_args.py`. The trained HNN will be stored in a tar file with the name the user specified in `get_args.py`.

* Then run, either `hnn_lmc.py`, `hnn_hmc.py`, `hnn_nuts_online.py` to, respectively, perform Langevin Monte Carlo, Hamiltonian Monte Carlo, and No-U-Turn Sampling with the trained HNN. Note that the user specified sampling parameters can be adjusted in these files.

* For No-U-Turn Sampling, an online error monitoring scheme as described in (https://arxiv.org/abs/2208.06120) is used. To turn this feature off, set the `hnn_threshold` parameter in `dnn_nuts_online.py` to a large value like 1000.



## Symplectic neural nets (sympnets) [literature: https://arxiv.org/abs/2001.03750]



* go to src/sympnets/

* Prerequisite: download the `learner` directory from https://github.com/jpzxshi/sympnets into the src/sympnets/ folder

* Training data generation: generate the pkl file generated from either src/dnns/ or src/hnns/ as described under DNNs or HNNs. Copy this pkl file to src/sympnets/ folder.

* Include the Hamiltonian of the required probability distribution in the `functions.py` file. Some example probability distributions are already included. For information on the Hamiltonian of a probability distribution, see https://arxiv.org/pdf/1206.1901.pdf%20http://arxiv.org/abs/1206.1901.pdf.

* Adjust the parameters in `get_args.py`

* In `main.py`, run the "Load data and train SympNet (LA or G)" portion of the code to load the training data and train an LA or G sympnet based.

* In `main.py`, adjust the options in "Sampling parameters" portion of the code

* In `main.py`, run "Sampling" portion of the code to perform Langevin Monte Carlo, Hamiltonian Monte Carlo, or No-U-Turn Sampling

* For No-U-Turn Sampling, an online error monitoring scheme as described in (https://arxiv.org/abs/2208.06120) is used. To turn this feature off, set the `hnn_threshold` parameter in `Sampling.py` to a large value like 1000.



## Neural ODES



Coming soon!!



# Author information



Som L. Dhulipala 



Computational Scientist in Uncertainty Quantification



Computational Mechanics and Materials department



Email: [email protected] 



Idaho National Laboratory



# Acknowledgements



The authors of the following open-source codes are thanked whose work is helpful to the **BIhNNs** repository:



* https://github.com/greydanus/hamiltonian-nn under the Apache License 2.0

* https://github.com/mfouesneau/NUTS under the MIT License

* https://github.com/jpzxshi/sympnets (no license information is provided)