Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations
Recovering analytical solutions of physical fields from visual observations is a fundamental yet underexplored capability for AI-assisted scientific reasoning. We study visual-to-symbolic analytical solution inference (ViSA) for two-dimensional linear steady-state fields: given field visualizations (and first-order derivatives) plus minimal auxiliary metadata, the model must output a single executable SymPy expression with fully instantiated numeric constants. We introduce ViSA-R2 and align it with a self-verifying, solution-centric chain-of-thought pipeline that follows a physicist-like pathway: structural pattern recognition solution-family (ansatz) hypothesis parameter derivation consi...
| Authors | Pengze Li, Jiaquan Zhang, Yunbo Long et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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