Foundation models have revolutionized language modeling, while whether this success is replicated in scientific computing remains unexplored. We present OmniArch, the first prototype aiming at solving multi-scale and multi-physics scientific computing problems with physical alignment. We addressed all three challenges with one unified architecture. Its pre-training stage contains a Fourier Encoder-decoder fading out the disharmony across separated dimensions and a Transformer backbone integrating quantities through temporal dynamics, and the novel PDE-Aligner performs physics-informed fine-tuning under flexible conditions. As far as we know, we first conduct 1D-2D-3D united pre-training on the PDEBench, and it sets not only new performance benchmarks for 1D, 2D, and 3D PDEs but also demonstrates exceptional adaptability to new physics via in-context and zero-shot learning approaches, which supports realistic engineering applications and foresight physics discovery.
@article{arxiv.2402.16014,
title = {OmniArch: Building Foundation Model For Scientific Computing},
author = {Tianyu Chen and Haoyi Zhou and Ying Li and Hao Wang and Chonghan Gao and Rongye Shi and Shanghang Zhang and Jianxin Li},
journal= {arXiv preprint arXiv:2402.16014},
year = {2025}
}