arXiv:2601.04051v3 Announce Type: replace Abstract: Symbolic regression aims to find symbolic expressions that describe datasets. Due to its inherent interpretability, symbolic regression (SR) is a powerful paradigm for scientific discovery. Recent advances have expanded SR to describe related phenomena using a single expression with varying sets of parameters, thereby introducing a single categorical variable. To illustrate, this enables the search for a single expression describing temperaturedependent viscosity across multiple fluids, while simultaneously identifying a distinct set of fluid
The continuous advancements in AI and machine learning techniques, particularly in interpretability and discovery-oriented applications, drive the development of methods like symbolic regression.
This development enhances the interpretability and generalizability of AI models, crucial for scientific discovery and making AI outputs more explainable and trustworthy for strategic decision-making.
Symbolic regression can now describe multiple related phenomena with a single adaptable expression, enabling more efficient and comprehensive AI-driven scientific modeling.
- · AI/ML researchers
- · Scientific research institutions
- · Industries relying on predictive modeling (e.g., pharmaceuticals, materials scie
- · Engineers using AI for system design
- · Traditional empirical modeling approaches
- · Black-box AI models in scientific discovery
Symbolic regression provides a more interpretable and adaptable way to model complex scientific data.
This improved interpretability could accelerate scientific discovery and the development of new materials and processes.
The ability to consolidate diverse phenomena under a single symbolic expression may lead to a more unified understanding of scientific principles across various domains.
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Read at arXiv cs.LG