The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

The main aim of the study is analyzing of pupils' knowledge in mathematics by adaptive
neuro fuzzy inference system (ANFIS) after implementation of distance learning application …

Rainfall and deep percolation of irrigation are the most important sources of recharge in
Khatoon-Abad plain, Kerman province, Iran. Artificial recharge is a practical strategy to …

The paper presents a numerical technique based on neural networks for generalized
Caputo-type fractional differential equations with and without delay. We employ the theory of …

The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the
search for novel dynamical system-inspired deep learning primitives; however, their …

We investigate the approximate solution of pantograph type functional differential equations
using neural networks. The methodology is based on the ideas of Lagaris et al, and itis …

Diffusion models have achieved remarkable results in image generation, and have similarly
been used to learn high-performing policies in sequential decision-making tasks. Decision …

In this paper, we investigate the possible treatment of a class of fractional-order delay
differential equations. In delay differential equations, the evolution of the state depends on …

Through their exciting features, hybrid nanofluids have found a key role in energy transport
applications that can be managed according to requirements. The hybrid nanofluid has a …

Data-driven learning of fractional difference equations is investigated in this paper. Firstly, a
multi-layer neural network is designed. Loss functions are constructed by use of the …