Symbolic regression is emerging as a promising machine learning method for learning
succinct underlying interpretable mathematical expressions directly from data. Whereas it …

Symbolic regression (SR) is a challenging task in machine learning that involves finding a
mathematical expression for a function based on its values. Recent advancements in SR …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …

Discovering mathematical equations that govern physical and biological systems from
observed data is a fundamental challenge in scientific research. We present a new physics …

Mathematical equations have been unreasonably effective in describing complex natural
phenomena across various scientific disciplines. However, discovering such insightful …

Symbolic regression searches for analytic expressions that accurately describe studied
phenomena. The main attraction of this approach is that it returns an interpretable model that …

Identifying the mathematical relationships that best describe a dataset remains a very
challenging problem in machine learning, and is known as Symbolic Regression (SR). In …

Learning symbolic expressions directly from experiment data is a vital step in AI-driven
scientific discovery. Nevertheless, state-of-the-art approaches are limited to learning simple …

The industrialization of catalytic processes requires reliable kinetic models for their design,
optimization and control. Mechanistic models require significant domain knowledge, while …