AutoPDEBench: Benchmarking LLM Auto-Research for Neural PDE Solver Design
cs.CE, cs.AI
Submitted: 2026-09-26
Updated: 2026-09-26
Terminology
Sources
- LitLLM: A Toolkit for Scientific Literature Review
- Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
- ShapeNet: An Information-Rich 3D Model Repository
- AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
- Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
- Robin: A multi-agent system for automating scientific discovery
- AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
- Towards Multi-spatiotemporal-scale Generalized PDE Modeling
- Learning Physical Dynamics with Subequivariant Graph Neural Networks
- GNOT: A General Neural Operator Transformer for Operator Learning
- DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
- MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents
- CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
- Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
- Multipole Graph Neural Operator for Parametric Partial Differential Equations
- Kosmos: An AI Scientist for Autonomous Discovery
- Neural Inverse Operators for Solving PDE Inverse Problems
- AlphaEvolve: A coding agent for scientific and algorithmic discovery
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- AI Idea Bench 2025: AI Research Idea Generation Benchmark
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