Foundation models have revolutionized knowledge acquisition across domains,
and our study introduces OmniArch, a paradigm-shifting approach designed for
building foundation models in multi-physics scientific computing. OmniArch's
pre-training involves a versatile pipeline that processes multi-physics
spatio-temporal data, casting forward problem learning into scalable
auto-regressive tasks, while our novel Physics-Informed Reinforcement Learning
(PIRL) technique during fine-tuning ensures alignment with physical laws.
Pre-trained on the comprehensive PDEBench dataset, OmniArch not only sets new
performance benchmarks for 1D, 2D and 3D PDEs but also demonstrates exceptional
adaptability to new physics via few-shot and zero-shot learning approaches. The
model's representations further extend to inverse problem-solving, highlighting
the transformative potential of AI-enabled Scientific Computing(AI4SC)
foundation models for engineering applications and physics discovery.

OmniArch 是一种用于构建多物理科学计算的基础模型的变革性方法，它通过多物理时空数据的全能管道进行预训练，并利用精调期间的新颖的物理知情强化学习技术确保与物理定律的一致性。在全面的 PDEBench 数据集上经过预训练的 OmniArch 不仅为 1D、2D 和 3D PDEs 设立了新的性能基准，而且通过少样本学习和零样本学习方法展现了对新物理的卓越适应性。该模型的表示还扩展到逆问题求解，突显了 AI 辅助科学计算 (AI4SC) 基础模型在工程应用和物理发现方面的变革潜力。