Geometry-aware Latent Autoregressive Generative Model for PDEs in Complex Domains
Solving multiphysics partial differential equations (PDEs) remains a major challenge in scientific computing, especially for highly complex $μ$m-scale tortuous geometries critical to energy and chemical engineering. We address this challenge by proposing a Geometry-aware Latent Autoregressive generative Model for PDEs (GeoLAMP), which solves physics within highly irregular and tortuous structures by decoupling flow and transport physics. GeoLAMP introduces a dual-encoder architecture on graph representations to jointly capture global topology and fine-scale geometric features, enabling an effective transition from real-space fields to compact latent representations. In the latent space, w...
| Authors | Zi Wang, Minghui Xu, Tapan Mukerji |
|---|---|
| Primary category | cs.LG |
| Published | 31 August 2026 |
| Last updated at source | 29 September 2026 |
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