Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning (InterpML)

Version 1.0, March 2024.

This repository contains python scripts accompanying the paper:
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
Tyler Chang, Andrew Gillette, Romit Maulik
2024

The following subdirectories are included:

• interpml contains our interpolation scripts used for all studies
• experiments contains scripts demonstrating our experiments with synthetic data
• airfoil contains scripts demonstrating our experiments with the UIUC airfoil dataset

Further instructions are provided in each subdirectory.

Setup

To make interpolation methods importable, use the setup.py file:

python3 -m pip install -e .

Citation information

If you are referring to this code in a publication, please cite the following paper:

Tyler Chang, Andrew Gillette, and Romit Maulik. Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning. Submitted. 2024. LLNL–JRNL–846568.

@article{CGM2024,
  author = {Chang, Tyler and Gillette, Andrew and Maulik, Romit},
  title = {Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning},
  journal = {Submitted. arXiv:2404.03586},
  year = {2024},
}

If you wish to cite the code specifically, please use:

DOE CODE ID: #125566

[ more details will be added soon ]

The DOI for this repository is: [ will be added soon ]

License

The code in this respository is distributed under the terms of the MIT license.

All new contributions must be made under the MIT license.

See LICENSE and NOTICE for details.

SPDX-License-Identifier: (MIT)

LLNL-CODE-862386