Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids
High-fidelity simulations of free-surface flows using Lagrangian methods such as the Particle Finite Element Method (PFEM) are computationally demanding due to continuous domain updates and repeated solution of the governing equations. This challenge is further amplified by non-Newtonian rheologies, where material nonlinearities increase computational cost. These limitations motivate the development of efficient surrogate models to approximate PFEM dynamics at reduced cost. While data-driven deep learning approaches are promising, a key challenge is designing models that operate on arbitrary and evolving geometries. We propose a self-attention-based neural surrogate for PFEM simulations o...
| Authors | Federico Lanteri, Massimiliano Cremonesi |
|---|---|
| Primary category | cs.CE |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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