In this paper, a hybrid semi-analytical–deep learning framework for the solution of nonlinear partial differential equations (PDEs) is proposed that integrates the New Iterative Method (NIM) with Physics-Informed Neural Networks (PINNs). Low–order NIM expansion provides an analytic baseline that meets the required init...
A. Alshehry, Saima Noor, Humaira Yasmin et al.· Scientific Reports· 0 citations
The present study numerically investigates the unsteady three-dimensional electromagnetohydrodynamic (EMHD) flow of a dual-fractional nanofluid (DFNF) comprising cobalt ferrite (CoFe2O4) and water flowing over an inclined porous rotating disk (PRD) using a combination of numerical simulation and soft computing techniqu...
A. Aldhafeeri, Z. Raizah, Humaira Yasmin et al.· Discover Nano· 0 citations
This paper presents a hybrid semi-analytical and physics-informed neural network framework for solving linear and nonlinear partial differential equations. The proposed approach combines the New Iterative Method (NIM) with Physics-Informed Neural Networks (PINNs): a low-order analytical approximation is generated by NI...
A. Alshehry, Saima Noor, Humaira Yasmin et al.· Scientific Reports· 0 citations
This paper presents a hybrid semi-analytical/deep-learning framework for nonlinear PDEs, combining the New Iterative Method (NIM) with Physics-Informed Neural Networks (PINNs). A truncated NIM series provides a closed-form baseline satisfying the initial condition exactly, while a neural network learns only the residua...
A. Alshehry, Saima Noor, Humaira Yasmin et al.· Scientific Reports· 0 citations
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