Symbolic Regression for Scientific Discovery
Symbolic regression is an approach to explain observed data, using symbolic expressions. Compared to deep-learning-based black-box methods, those expressions are relatively human understandable, which is critical especially when non-machine-learning (ML) experts apply ML to problems in their research domains such as physics, chemistry, and material science. We discuss the potential of symbolic regression for scientific discovery (SRSD) applications, proposing new SRSD datasets and evaluation metrics.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
NeurIPS 2022 AI for Science Workshop