Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

The authors showcase the potential of symbolic regression as an analytic method for use in
materials research. First, the authors briefly describe the current state-of-the-art method …

Many real‐world scientific processes are governed by complex non‐linear dynamic systems
that can be represented by differential equations. Recently, there has been an increased …

Scientific research's mandate is to comprehend and explore the world, as well as to improve
it based on experience and knowledge. Knowledge embedding and knowledge discovery …

Partial differential equations (PDEs) are concise and understandable representations of
domain knowledge, which are essential for deepening our understanding of physical …

Data-driven methods have recently been developed to discover underlying partial
differential equations (PDEs) of physical problems. However, for these methods, a complete …

Data-driven methods have recently made great progress in the discovery of partial
differential equations (PDEs) from spatial-temporal data. However, several challenges …

Data-driven methods provide model creation tools for systems where the application of
conventional analytical methods is restrained. The proposed method involves the data …

Data-driven discovery of partial differential equations (PDEs) has recently made tremendous
progress, and many canonical PDEs have been discovered successfully for proof of …

Data-driven discovery of partial differential equations (PDEs) has attracted increasing
attention in recent years. Although significant progress has been made, certain unresolved …