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MAD-Operator-Learning

Operator learning with MAD: Training datasets and scripts for PDE solutions.

Overview

This repository contains the datasets and code for implementing the Mathematical Artificial Data (MAD) paradigm, designed for solving partial differential equations (PDEs) such as Poisson, Helmholtz, and Laplace equations. The MAD framework provides a modular, scalable, and efficient approach to operator learning.

Features

  • Analytical dataset generation for various PDEs.
  • Training and evaluation of MAD and PINN-based models.
  • Pre-trained models for quick evaluation and reproducibility.

File Structure

  • data/: Contains training and test datasets.
  • models/: Stores pre-trained models for reproducibility. Users can delete these models to train from scratch.
  • scripts/: Contains scripts for different functionalities:
    • data_generation/: Scripts for generating datasets for various PDEs (e.g., Laplace, Poisson, Helmholtz equations).
    • model_training/: Scripts for training MAD and PINN models on the generated datasets.
    • model_testing/: Scripts for testing trained models and visualizing performance.

Quick Start

To quickly test the pre-trained MAD and PINN models, follow these steps:

  1. Clone this repository:

    git clone https://github.com/bzlu-Group/MAD-Operator-Learning.git
cd MAD-Operator-Learning

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the test script:

    python scripts/model_testing/testlaplace2D.py

This will load the pre-trained models from the models/ directory, evaluate them on a predefined dataset, and display the results.

Advanced Usage

If you'd like to regenerate the datasets, retrain the models, or train them from scratch, follow the instructions below:

Step 1: Generate Dataset

Use the scripts in the data_generation folder to generate new training and testing datasets. For example, to generate data for the Helmholtz equation:

python scripts/data_generation/gMADpoisson2D.py

The generated datasets will be saved in the data/ directory.

Step 2: Train Models

If you want to train models from scratch, delete the pre-trained models in the models/ directory and run the training scripts. For example:

python scripts/model_training/trainMADpoisson2D.py

Note: Training on a personal laptop might require reducing the batch_size parameter to avoid GPU memory issues.

Similarly, to train a PINN-based model:

python scripts/model_training/trainPINNlaplace2D.py

Step 3: Test Models

After training, you can test the models using the model_testing scripts:

python scripts/model_testing/testlaplace2D.py

This script will evaluate the trained models on the test datasets and generate visualizations for the predictions and errors.

Pre-trained Models

Pre-trained models are provided in the models/ directory for quick evaluation:

  • MAD1helmholtz2D_(2000,51).pth: Pre-trained MAD model for solving Helmholtz equations with 2000 functions and 51x51 grid resolution.
  • PINN1helmholtz2D_(2000,51).pth: PINN-based model trained on the same dataset for comparison.

These models can be directly loaded and tested using the model_testing scripts. If you'd like to retrain the models, simply delete these files and follow the training instructions above.

Requirements

The following Python libraries are required:

  • torch>=2.4.1
  • numpy>=1.26.3
  • matplotlib>=3.9.2
  • scipy>=1.14.1
  • scikit-learn>=1.5.1

Install dependencies using:

pip install -r requirements.txt

Notes

  • The specified versions are based on the development environment. Lower versions might work, but are not guaranteed. If you encounter issues with compatibility, consider updating to the listed versions.
  • Standard Python libraries like time, os, random, and math are used but do not require additional installation.

Notes on System Requirements

  • GPU Memory: Training MAD and PINN models with default parameters requires significant GPU memory. If running on a personal laptop, consider reducing the batch_size parameter to avoid out-of-memory errors.
  • Pre-trained Models: If you only want to evaluate the models, you can directly use the pre-trained models provided in the models/ folder without retraining.

FAQ

  1. What if I encounter GPU out-of-memory errors during training?

    • Reduce the batch_size parameter in the training scripts.
    • Use a smaller dataset by reducing the num_samples parameter during dataset generation.

  2. Can I use this repository for other PDEs?

    • Yes, the framework is modular and can be adapted to other PDEs by modifying the dataset generation and model training scripts.

  3. Why does the model testing script fail to run?

    • Ensure that the pre-trained models are present in the models/ directory. If you have deleted them, you need to retrain the models using the training scripts.

  4. What if I want to use a custom dataset?

    • You can modify the dataset generation scripts in the data_generation folder to create datasets tailored to your problem.

License

This project is licensed under the MIT License.