Interpretable scientific discovery with symbolic regression: a review
importance 6/10
ai assisted
Evolutionary Algorithms and Applications scientific machine learning stage: research
Problem
Scientists need interpretable mathematical expressions learned directly from data, traditionally tackled with genetic programming.
Why it matters
Interpretable models let scientists understand and trust data-driven discoveries across fundamental and applied sciences.
What the AI did
Survey of symbolic regression, including deep learning approaches, as a data-driven model discovery tool, organizing and assessing applications across domains.
Results
not stated in source
Impact
The review structures state-of-the-art symbolic regression applications and assesses their effectiveness.
Record
| Venue | Artificial Intelligence Review |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Nour Makke, Sanjay Chawla |
| Citations | 330 (OpenAlex, at import time) |
| References | 50 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s10462-023-10622-0 |
| Open access | hybrid |
| Keywords | symbolic regression, interpretable machine learning, scientific discovery, model discovery, deep learning |
Importance rating
6
significant — thousands to millions affected
Interpretable model discovery spans fundamental to applied sciences per abstract, a broadly significant scientific capability, but no scale stated.