Sobolev Training for Operator Learning
<|reference_start|>Sobolev Training for Operator Learning: This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance. Our research reveals that integrating derivative information into the loss function enhances the training process, and we propose a novel framework to approximate derivatives on irregular meshes in operator learning. Our findings are supported by both experimental evidence and theoretical analysis. This demonstrates the effectiveness of Sobolev Training in approximating the solution operators between infinite-dimensional spaces.<|reference_end|>
@article{cho2024sobolev,
    title={Sobolev Training for Operator Learning},
    author={Namkyeong Cho, Junseung Ryu, Hyung Ju Hwang},
    journal={arXiv preprint arXiv:2402.09084},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09084},
    primaryClass={cs.LG cs.AI}
}