{"ID":2842497,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10829","arxiv_id":"2511.10829","title":"Towards Universal Neural Operators through Multiphysics Pretraining","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.10829","url_pdf":"https://arxiv.org/pdf/2511.10829v1","authors":"[\"Mikhail Masliaev\",\"Dmitry Gusarov\",\"Ilya Markov\",\"Alexander Hvatov\"]","published":"2025-11-13T22:04:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}