5 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
Quantum Physics-Informed Neural Networks for Solving Integro and Fractional PDEs
Deepak Gupta, Ratikanta Behera
Quantum neural networks have emerged as powerful models for approximating nonlinear functions. Yet their use in solving integro-differential equations (IDEs) and fractional integro…
Quaternion Nonlinear Transform-Induced Nuclear Norm for Low-Rank Tensor Completion
Biswarup Karmakar, Ratikanta Behera
Tensor completion has emerged as a powerful framework for recovering missing data in multidimensional signals by exploiting low-rank tensor structures. Among existing approaches, l…
A Family of Iterative Methods for Computing Generalized Inverses of Quaternion Matrices and its Applications
Biswarup Karmakar, Neha Bhadala, Ratikanta Behera
The computation of generalized inverses of quaternion matrices is a fundamental problem in quaternion linear algebra, with wide-ranging applications in signal processing, image res…
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…
Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization
Biswarup Karmakar, Ratikanta Behera
The robust low-rank tensor completion problem addresses the challenge of recovering corrupted high-dimensional tensor data with missing entries, outliers, and sparse noise commonly…
A hybrid wavelet-based physics-informed neural network for portfolio management
Bahadur Yadav, Mahaprasad Mohanty, Ratikanta Behera +1
In this paper, we present a Hybrid Wavelet-based Physics-Informed Neural Networks (HW-PINNs) framework for portfolio management that provides a promising alternative to Physics-Inf…
Second-Generation Wavelet-inspired Tensor Product with Applications in Hyperspectral Imaging
Aneesh Panchal, Ratikanta Behera
This paper introduces the -product, a novel wavelet-based tensor multiplication scheme leveraging second-generation wavelet transforms to achieve linear transformation complexit…
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,…
RGDBEK: Randomized Greedy Double Block Extended Kaczmarz Algorithm with Hybrid Parallel Implementation and Applications
Aneesh Panchal, Ratikanta Behera
Kaczmarz is one of the most prominent iterative solvers for linear systems of equations. Despite substantial research progress in recent years, the state-of-the-art Kaczmarz algori…
Structure Preserving Algorithms for Quaternion Outer Inverses with Applications
Neha Bhadala, Ratikanta Behera
This study investigates the theoretical and computational aspects of quaternion generalized inverses, focusing on outer inverses and {1,2}-inverses with prescribed range and/or nul…
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…