PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations

• URL: http://arxiv.org/abs/2505.24717v1
• Date: Fri, 30 May 2025 15:39:54 GMT
• Title: PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
• Authors: Benjamin Holzschuh, Qiang Liu, Georg Kohl, Nils Thuerey,
• Abstract summary: We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids.<n>We demonstrate that our proposed architecture outperforms state-of-the-art transformer architectures for computer vision on a large dataset of 16 different types of PDEs.
• Score: 23.196500975208302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids. We combine recent architectural improvements of diffusion transformers with adjustments specific for large-scale simulations to yield a more scalable and versatile general-purpose transformer architecture, which can be used as the backbone for building large-scale foundation models in physical sciences. We demonstrate that our proposed architecture outperforms state-of-the-art transformer architectures for computer vision on a large dataset of 16 different types of PDEs. We propose to embed different physical channels individually as spatio-temporal tokens, which interact via channel-wise self-attention. This helps to maintain a consistent information density of tokens when learning multiple types of PDEs simultaneously. We demonstrate that our pre-trained models achieve improved performance on several challenging downstream tasks compared to training from scratch and also beat other foundation model architectures for physics simulations.

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