arXiv:2511.04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize minimizing prediction error over identifying the governing equations, often producing overly complex or inaccurate expressions. To address this, we present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models, genetic algorithms (GAs), and genetic programming (GP). In particular, our explainable SR method disti
The increasing complexity of AI models and the demand for interpretability in scientific discovery necessitate more sophisticated symbolic regression techniques at this moment.
This research advances the ability of AI to not only predict but also explain phenomena, which is crucial for scientific understanding, automation of discovery, and robust AI systems.
The development of decomposable symbolic regression could lead to more accurate and interpretable mathematical models derived directly from data, enhancing scientific research and engineering.
- · AI researchers
- · Scientific R&D
- · Healthcare
- · Material science
- · Manual model building
- · Opaque AI systems
Increased pace of scientific discovery through automated hypothesis generation and validation.
Improved industrial process optimization and control due to better understanding of underlying physical laws.
New drugs or materials discovered faster, leading to significant economic and societal benefits.
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Read at arXiv cs.LG