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+# Flow_Perturbation
+We introduce the flow perturbation method, which incorporates optimized stochastic perturbations into the flow. By reweighting trajectories generated by the perturbed flow, our method achieves
+unbiased sampling of the Boltzmann distribution with orders of magnitude speedup compared to both brute force
+Jacobian calculations and the Hutchinson estimator. 
+
+![Flow Perturbation](./data/ODE_pf.png "Title")
+
+Publication
+-----------
+Please find the arxiv preprint here:
+[https://arxiv.org/abs/2002.06707](https://arxiv.org/abs/2407.10666)
+
+Citation update is coming up...
+```
+@article{peng2024flow,
+  title={Flow Perturbation to Accelerate Unbiased Sampling of Boltzmann distribution},
+  author={Peng, Xin and Gao, Ang},
+  journal={arXiv preprint arXiv:2407.10666},
+  year={2024}
+}
+```
+***
+# Dependencies
+* Mandatory
+  * [pytorch>=2.0](https://github.com/pytorch/pytorch)
+  * [numpy](https://github.com/numpy/numpy)
+  * [bgmol](https://github.com/noegroup/bgmol) (for  Chignolin)
+  * [bgflow](https://github.com/noegroup/bgmol) (for  Chignolin)
+  * [mdtraj=1.9.9](https://github.com/mdtraj/mdtraj) (for  Chignolin)
+
+## Training
+* All hyper-parameters and training details are provided in config files (), and free feel to tune these parameters../configs/*.yml
+  
+```bash
+python train.py ./configs/GMM10D_default.yml ./models/GMM10D
+python train.py ./configs/GMM1000D_default.yml ./models/GMM1000D
+python train.py ./configs/CGN_default.yml ./models/CGN
+```
+
+* The model checkpoints will be saved in the specified directory,e.g., ./models/GMM10D
+
+## Metropolis Monte Carlo (MC) simulations
+* The Metropolis MC simulations are provided in the following files:
+
+```bash
+python MC.py ./configs/CGN_default.yml ./models/CGN --method 0 --eps_type Rademacher
+```
+
+* The first argument specifies the configuration file, while the second argument indicates the model directory. The `--method` option determines the approach for running the Monte Carlo (MC) simulations:
+ - `0`: Flow Perturbation (FP)
+  - `-1`: Jacobian-based method
+  - `-2`: Stochastic Normalizing Flow (SNF)
+  - `1-n`: Hutchinson trace estimator
+- `--eps_type` parameter: Defines the type of perturbation to use, such as Rademacher, Gaussian, etc.
+
+## Sequential Monte Carlo (SMC)
+* The Sequential Monte Carlo in the following files:
+```bash
+python SMC.py ./configs/GMM10D_default.yml ./models/GMM10D --method 0 --eps_type Rademacher
+python SMC.py ./configs/GMM1000D_default.yml ./models/GMM1000D --method 0 --eps_type Rademacher
+python SMC.py ./configs/CGN_default.yml ./models/CGN --method 0 --eps_type Rademacher
+```
+* The first argument specifies the configuration file, while the second argument indicates the model directory. The `--method` option determines the approach for running the Sequential Monte Carlo (SMC) simulations:
+ - `0`: Flow Perturbation (FP)
+  - `-1`: Jacobian-based method
+  - `-2`: Stochastic Normalizing Flow (SNF)
+  - `1-n`: Hutchinson trace estimator
+- `--eps_type` parameter: Defines the type of perturbation to use, such as Rademacher, Gaussian, etc.
+
+
+## Model and Data
+* The result datasets presented in the paper can be found in the `Result` folder of this repository.
+
+* We provide all pre-trained model checkpoints as well as other result datasets from the paper. These resources are hosted on Hugging Face:
+  * [Model and Result Folder](https://huggingface.co/XinPeng76/Flow_Perturbation)
+
+
+## [License](#dependencies)
+[MIT License](LICENSE)