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arXiv · 2511.10829

Towards Universal Neural Operators through Multiphysics Pretraining

Mikhail Masliaev·Dmitry Gusarov·Ilya Markov·Alexander Hvatov
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Abstract

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.

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Mikhail Masliaev, Dmitry Gusarov, Ilya Markov, Alexander Hvatov. 2025-11-13. Towards Universal Neural Operators through Multiphysics Pretraining. https://arxiv.org/abs/2511.10829

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