Recent advancements in neural network design have given rise to the development of
Kolmogorov-Arnold Networks (KANs), which enhance interpretability and precision of these …

In this research, an attempt has been made to build a bridge between a machine learning
model called Least-Squares Support Vector Regression (LS-SVR) and Weighted Residual …

This paper presents a novel physics-informed machine learning approach, designed to
approximate solutions to a specific category of Thomas–Fermi differential equations. To …

The current climate change is causing the upwelling phenomenon to occur frequently in
lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating …

In this paper, a new deep-learning architecture for solving the non-linear Falkner-Skan
equation is proposed. Using Legendre and Chebyshev neural blocks, this approach shows …

The rapid development of marine and urban infrastructure led to the extensive studies on
seawater sea sand concrete (SWSSC) filled fiber reinforced polymer (FRP)/steel tubes. The …

This paper considers the hyperparameter optimization problem of mathematical techniques
that arise in the numerical solution of differential and integral equations. The well-known …

The burgeoning intersection of machine learning (ML) with electrochemical sensing heralds
a transformative era in analytical science, pushing the boundaries of what's possible in …

In an educational setting, a teacher plays a crucial role in various classroom teaching
patterns. Similarly, mirroring this aspect of human learning, the learning using privileged …

In this paper, an innovative numerical methodology is introduced for solving multiple types of
linear one-dimensional Fredholm integro-differential equations using least squares support …