Towards Physics-Guided Foundation Models

Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downstream tasks. However, traditional foundation models struggle with out-of-distribution prediction and can produce outputs that are unrealistic and physically infeasible. We propose the notation of physics-guided foundation models (PGFM), that is, foundation models integrated with broad or general domain (e.g., scientific) physical knowledge applicable to a wide range of downstream tasks.

@article{farhadloo2025_2502.15013,
  title={ Towards Physics-Guided Foundation Models },
  author={ Majid Farhadloo and Arun Sharma and Mingzhou Yang and Bharat Jayaprakash and William Northrop and Shashi Shekhar },
  journal={arXiv preprint arXiv:2502.15013},
  year={ 2025 }
}