Author

P. Das

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Preprint Aug 2026

ENPINN: Energy-Norm-Guided Gradient-Enhanced PINNs for Generalized Transport Problems with Sharp Gradients

Physics-informed neural networks (PINNs) have emerged as a meshless alternative to conventional numerical methods for solving partial differential equations (PDEs). However, their limited ability to capture sharp gradients can lead to substantial errors when resolving boundary and interior layers. Here, we introduce an...

S. Maity, P. Das, Arihant Patawari et al. · 2 citations

Aug 2026

A semi-analytical fractional order neural network framework for two-dimensional time fractional reaction-diffusion problems.

It is observed that the proposed ADM-PINN approach is highly effective for several two-dimensional time fractional problems including the Schrodinger equation for convergent approximation of the computed solution.

Arihant Patawari, P. Das, Subrata Rana · Neural Networks · 1 citation