The working mechanisms of complex natural systems tend to abide by concise partial
differential equations (PDEs). Methods that directly mine equations from data are called PDE …

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 …

Symbolic regression, a multifaceted optimization challenge involving the refinement of both
structural components and coefficients, has gained significant research interest in recent …

Data increasingly abounds, but distilling their underlying relationships down to something
interpretable remains challenging. One approach is genetic programming …

Biologically, the brain does not rely on a single type of neuron that universally functions in all
aspects. Instead, it acts as a sophisticated designer of task-based neurons. In this study, we …

Symbolic regression with polynomial neural networks and polynomial neural ordinary
differential equations (ODEs) are two recent and powerful approaches for equation recovery …

Brine-gas interfacial tension (γ) is an important parameter to determine fluid dynamics,
trapping and distributions at pore-scale, thus influencing gas (H2, CH4 and CO2) geo …

Interleaved practice enhances the memory and problem-solving ability of students in
undergraduate courses. We introduce a personalized learning tool built on a Large …

A significant role in clinical treatment and educational tasks is played by clinical image
classification. However, the traditional approach has reached its peak in terms of …

In this thesis, we explore the field of Symbolic Regression (SR), a middle ground between
simple linear regression and complex inscrutable black box regressors such as neural …