  We introduce multiple physics pretraining (MPP), an autoregressive
task-agnostic pretraining approach for physical surrogate modeling. MPP
involves training large surrogate models to predict the dynamics of multiple
heterogeneous physical systems simultaneously by learning features that are
broadly useful across diverse physical tasks. In order to learn effectively in
this setting, we introduce a shared embedding and normalization strategy that
projects the fields of multiple systems into a single shared embedding space.
We validate the efficacy of our approach on both pretraining and downstream
tasks over a broad fluid mechanics-oriented benchmark. We show that a single
MPP-pretrained transformer is able to match or outperform task-specific
baselines on all pretraining sub-tasks without the need for finetuning. For
downstream tasks, we demonstrate that finetuning MPP-trained models results in
more accurate predictions across multiple time-steps on new physics compared to
training from scratch or finetuning pretrained video foundation models. We
open-source our code and model weights trained at multiple scales for
reproducibility and community experimentation.
