arXiv:2606.09276v1 Announce Type: new Abstract: Equation discovery aims to automate the discovery of scientific models in the form of mathematical equations from data. Technically, equation discovery is implemented by symbolic regression algorithms. Performance of symbolic regression for equation discovery is measured along two dimensions: Prediction accuracy on test data, and recovery of known groundtruth formulas. For standard regression, accuracy is typically measured on in-domain test data, for instance, by splitting a data set randomly into training and test data. While this makes sense f
The proliferation of complex datasets from scientific and engineering domains necessitates more robust automation in model discovery, making equation discovery benches like ERBench crucial for evaluating progress.
Improved benchmarks for equation discovery algorithms directly contribute to the automation of scientific research, potentially accelerating breakthroughs across various AI-intensive fields beyond standard prediction tasks.
The explicit focus on both prediction accuracy and groundtruth formula recovery in a dedicated benchmark refines how symbolic regression algorithms are evaluated, shifting the emphasis beyond mere predictive power towards true scientific understanding.
- · AI researchers (symbolic regression)
- · Scientific R&D sectors
- · AI/ML tooling providers
- · Manual model discovery processes
- · Inefficient symbolic regression algorithms
More rigorous development and evaluation of AI systems for scientific model generation.
Accelerated discovery of new physical, chemical, or biological laws through more effective AI techniques.
Reduced time and cost in basic scientific research, leading to faster innovation cycles in applied fields like medicine or materials science.
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Read at arXiv cs.LG