Building Flexible Machine Learning Models for Scientific Computing at Scale
CoRR(2024)
摘要
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.
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