This repository contains companion code for "Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed Neural Networks". We provide instructions for reproducing our experiments and plots.
To install requirements:
anaconda:
conda install -r environment.yml
docker:
docker pull ghcr.io/nvidia/jax:equinox
To generate data, you can use the current dataset in data.py or you can add new data.
To train the model(s) in the paper, change the directory to the specific directory,
for example, run command for approximation:
cd ./approximation/
python approximation_1d.py --mode train
To evaluate the model(s) in the paper, change the directory to the specific directory,
for example, run command for approximation:
cd ./approximation/
python approximation_1d.py --mode eval
We demostrate partial results of our paper:
| Model name | MLP | KAN | SincKAN |
|---|---|---|---|
| pbl | 2.89e-2 ± 3.09e-2 | 4.48e-3 ± 4.20e-3 | 1.88e-3 ± 8.55e-4 |
| bl_1000 | 9.87 ± 8.70 | 11.3 ± 8.79 | 5.48e-3 ± 3.45e-3 |