This research confronts the challenge of substantial physical equation discrepancies encountered in the generation of spatiotemporal physical fields through data-driven trained models. A spatiotemporal physical field generation model, named HMT-PF, is developed based on the hybrid Mamba-Transformer architecture, incorporating unstructured grid information as input. A fine-tuning block, enhanced with physical information, is introduced to effectively reduce the physical equation discrepancies. The physical equation residuals are computed through a point query mechanism for efficient gradient evaluation, then encoded into latent space for refinement. The fine-tuning process employs a self-supervised learning approach to achieve physical consistency while maintaining essential field characteristics. Results show that the hybrid Mamba-Transformer model achieves good performance in generating spatiotemporal fields, while the physics-informed fine-tuning mechanism further reduces significant physical errors effectively. A MSE-R evaluation method is developed to assess the accuracy and realism of physical field generation.
View on arXiv@article{du2025_2505.11578,
title={ Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning },
author={ Peimian Du and Jiabin Liu and Xiaowei Jin and Mengwang Zuo and Hui Li },
journal={arXiv preprint arXiv:2505.11578},
year={ 2025 }
}