uPDEt: Efficient Transformer for Unstructured Physics Simulations

Deep learning surrogate models have emerged as powerful tools for accelerating Partial Differential Equation (PDE) simulations on complex, unstructured domains and arbitrary geometries. However, existing state-of-the-art architectures often exhibit excessive memory consumption, steep computational overhead, and limited capacity to perform autoregressive dynamics. In this work, we introduce uPDEt, a highly efficient and scalable Transformer-based neural operator designed for unstructured physical systems. uPDEt yields highly competitive accuracy while, at the same time, significantly reducing memory footprint and runtime. This results in substantially reduced wall-clock training and inference times compared to top-performing models. With this pairing of high accuracy and high efficiency, uPDEt outperforms existing unstructured neural operators in terms of Pareto optimality across a range of complex 3D benchmarks. Furthermore, while many unstructured models are primarily restricted to steady-state predictions, uPDEt extends to autoregressive rollouts for transient dynamics. Our results highlight uPDEt as a versatile, high-performance foundation for large-scale physical surrogate modeling.