# Towards Universal Neural Operators through Multiphysics Pretraining

arXiv: https://arxiv.org/abs/2511.10829 · HTML: https://mlsanity.com/paper/2511.10829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale](https://mlsanity.com/paper/2605.12343/) (2026)
- [MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers](https://mlsanity.com/paper/2606.04366/) (2026)

## Abstract (arXiv)

Although neural operators are widely used in data-driven physical simulations, their training remains computationally expensive. Recent advances address this issue via downstream learning, where a model pretrained on simpler problems is fine-tuned on more complex ones. In this research, we investigate transformer-based neural operators, which have previously been applied only to specific problems, in a more general transfer learning setting. We evaluate their performance across diverse PDE problems, including extrapolation to unseen parameters, incorporation of new variables, and transfer from multi-equation datasets. Our results demonstrate that advanced neural operator architectures can effectively transfer knowledge across PDE problems.