HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
This addresses the problem of solving PDEs with neural networks for researchers and practitioners in computational physics and engineering, though it appears incremental as it builds on existing graph transformer and neural operator concepts.
The authors tackled solving partial differential equations (PDEs) using neural networks by developing HAMLET, a graph transformer framework that incorporates differential equation information modularly, resulting in outperforming current techniques in experiments.
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorporate differential equation information into the solution process. This modularity enhances parameter correspondence control, making HAMLET adaptable to PDEs of arbitrary geometries and varied input formats. Notably, HAMLET scales effectively with increasing data complexity and noise, showcasing its robustness. HAMLET is not just tailored to a single type of physical simulation, but can be applied across various domains. Moreover, it boosts model resilience and performance, especially in scenarios with limited data. We demonstrate, through extensive experiments, that our framework is capable of outperforming current techniques for PDEs.