SPIN Unprocessed September 2, 2026 ai_technology research
Geometry-aware Latent Autoregressive Generative Model for PDEs in Complex Domains
View original on arxiv.orgOverview
arXiv:2609.00297v1 Announce Type: new Abstract: Solving multiphysics partial differential equations (PDEs) remains a major challenge in scientific computing, especially for highly complex $\mu$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) for solving physics within highly irregular and tortuous structures. GeoLAMP introduces a dual-encoder architecture on g
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- QTEA: Ternary LLMs with Sparse Residual Salient Weight and By-Column Optimization
- WHALE: A Simple Recipe for Joint Harness-Weight Optimization
- Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
- Flawed in Nature, Perfect through Evolution
- Good Memory Has ECC: Evaluating the Memory of Vision-Language Models Beyond Accuracy
- Generative artificial intelligence for reliable mechanistic reasoning for corrosion
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO