SOURCE-LINKED INTELLIGENCE

See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Discovering governing partial differential equations (PDEs) from observational data remains a core challenge across the sciences. Existing sparse-regression, symbolic-regression, and LLM-based approaches can be constrained by predefined libraries, noise sensitivity, hallucination, or limited iterative refinement. We introduce \textbf{MAGE} (\textbf{M}ultimodal \textbf{A}gentic \textbf{G}overning \textbf{E}quation Discovery), an agentic framework that organizes PDE discovery as a \textit{confidence governed hypothesis validation loop} inspired by the scientific cycle of observation, hypothesis,

Read original source ↗ Open in workspace

recordType

paper

region

Global