AutoPDEBench: Benchmarking LLM Auto-Research for Neural PDE Solver Design
Partial differential equations (PDEs) are essential for modeling complex physical systems, and neural solvers have recently emerged as powerful data-driven tools for numerically solving them. However, existing neural solvers struggle with domain-specific challenges, such as varying parameters and high-speed flows, necessitating specialized architectures. Manually designing these specialized solver architectures is a highly iterative, time-consuming process requiring deep expertise, creating a significant bottleneck in scientific discovery. We propose leveraging autonomous AI research agents to automate the synthesis of specialized solvers. To support this, we introduce AutoPDEBench, a ben...
| Authors | Ruoyan Li, Wei Wang, Yizhou Sun |
|---|---|
| Primary category | cs.CE |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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