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# Scientific equation discovery runner

Search for symbolic formulas that fit experimental scientific datasets

[Use with BotBento ](https://app.botbento.com/?ref=usecases&utm_source=botbento.com&utm_medium=usecases&utm_campaign=scientific-equation-discovery-runner)Copy promptShare[Markdown](/text/usecases/scientific-equation-discovery-runner/index.md)[العربية](/ar/usecases/scientific-equation-discovery-runner/)

Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askSubmit dataset and domain spec
- ThenRun equation search loop
- ResultExport candidate formulas

Automate symbolic regression workflows by taking experimental tabular data, generating candidate mathematical program skeletons, and optimizing free parameters against target variables. Track search iterations, compare validation error metrics across hypothesized equations, and compile the top-performing candidate formulas into research summaries.

## Prompt to start with

BotBentoRecommended

```
When given a target dataset and domain specifications, inspect the input columns and configure the search script. Execute the regression discovery pipeline in the terminal, monitoring optimization steps and convergence loss. Log candidate equation skeletons and their fitted coefficients into a note. Always ask for user confirmation before deleting temporary run artifacts, killing running processes, or overwriting existing equation files.
```

Copy

## Tools it uses

TerminalSpreadsheetNotes

## Works with

BotBento

## Tags

- symbolic-regression
- scientific-computing
- data-analysis
- python

## Source

[LLM-SR: \[ICLR 2025 Oral\] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Larg](https://github.com/deep-symbolic-mathematics/LLM-SR)
GitHub · checked 2026-10-16

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-16 · Updated 2026-10-16 · [Suggest a correction](/contact/)

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