Gaussian Process Operator (GPO)

Description

This repository contains the implementation code for the paper "Towards Gaussian Process for operator learning: An uncertainty aware resolution independent operator learning algorithm for computational mechanics" here.

Folders & Files

The repository is organized as follows:

• data: Contains the datasets.
• experiments: Each sub-directory contains the specific code for various case studies
• general: Utils and additional resources.
• requirement.txt: additional packages required.

Getting Started

Prerequisites

Before running the code, ensure you have the following installed:

• Python 3.8+
• PyTorch 1.7+
• Additional dependencies listed in requirements.txt

Installation

Clone the repository and install the required Python packages:

git clone https://github.com/yourusername/your-repository-name.git
cd your-repository-name
pip install -r requirements.txt

Dataset

• The training and testing datasets for different case studies are available in the following link:

  Dataset \

BibTex

If you use any part our codes, please cite us at,

@misc{kumar2024gaussianprocessoperatorlearning,
      title={Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics}, 
      author={Sawan Kumar and Rajdip Nayek and Souvik Chakraborty},
      year={2024},
      eprint={2409.10972},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/2409.10972}, 
}