5 citations · 5 across the 4 of their papers we have counts for
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LiNO: Lifting based multiresolution neural operator
Himanshu Pandey, Subham Patel, Ratikanta Behera
Recently, neural operators have shown promising outcomes for learning solution operators of differential equations directly from data. This framework learns a functional mapping fr…
An adaptive wavelet-based PINN for problems with localized high-magnitude source
Himanshu Pandey, Ratikanta Behera
In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer from two fundamental limitation…
Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
Deepak Gupta, Himanshu Pandey, Ratikanta Behera
This work proposes a wavelet-based physics-informed quantum neural network framework to efficiently address multiscale partial differential equations that involve sharp gradients,…
An efficient wavelet-based physics-informed neural network for multiscale problems
Himanshu Pandey, Anshima Singh, Ratikanta Behera
Physics-informed neural networks (PINNs) are a class of deep learning models that utilize physics in the form of differential equations to address complex problems, including those…