PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations

TL;DR

PI-MFM is a physics-informed multimodal foundation model that efficiently learns PDE solutions across diverse families by directly incorporating physical laws during training, outperforming data-driven methods especially with limited data.

Contribution

This work introduces a novel physics-informed framework for multimodal foundation models that enforces PDE physics during pretraining and adaptation, enabling scalable, data-efficient PDE solving.

Findings

Outperforms data-driven models on 13 PDE families, especially with sparse data.

Physics losses improve robustness against noise.

Zero-shot fine-tuning reduces errors to around 1% without labeled solutions.

Abstract

Partial differential equations (PDEs) govern a wide range of physical systems, and recent multimodal foundation models have shown promise for learning PDE solution operators across diverse equation families. However, existing multi-operator learning approaches are data-hungry and neglect physics during training. Here, we propose a physics-informed multimodal foundation model (PI-MFM) framework that directly enforces governing equations during pretraining and adaptation. PI-MFM takes symbolic representations of PDEs as the input, and automatically assembles PDE residual losses from the input expression via a vectorized derivative computation. These designs enable any PDE-encoding multimodal foundation model to be trained or adapted with unified physics-informed objectives across equation families. On a benchmark of 13 parametric one-dimensional time-dependent PDE families, PI-MFM consistently outperforms purely data-driven counterparts, especially with sparse labeled spatiotemporal points, partially observed time domains, or few labeled function pairs. Physics losses further improve robustness against noise, and simple strategies such as resampling collocation points substantially improve accuracy. We also analyze the accuracy, precision, and computational cost of automatic differentiation and finite differences for derivative computation within PI-MFM. Finally, we demonstrate zero-shot physics-informed fine-tuning to unseen PDE families: starting from a physics-informed pretrained model, adapting using only PDE residuals and initial/boundary conditions, without any labeled solution data, rapidly reduces test errors to around 1% and clearly outperforms physics-only training from scratch. These results show that PI-MFM provides a practical and scalable path toward data-efficient, transferable PDE solvers.