7 papers
Fourier-Feature-Embedded Complex-Valued Physics-Informed Convolutional Residual Networks for Sparse and Gappy Noisy Flow-Field Reconstruction
Biswanath Barman, Rajendra K. Ray
Reconstructing flow fields from sparse and noisy measurements becomes particularly challenging when data are completely absent from dynamically critical regions, such as the vortex…
Split Complex-Valued Physics-Informed Neural Networks for Forward and Inverse Nonlinear Partial Differential Equations
Biswanath Barman, Rajendra K. Ray, Debdeep Chatterjee
Physics-informed neural networks (PINNs) can solve forward and inverse PDE problems but may struggle with spectral bias and oscillatory, high-frequency, or phase-dependent solution…
A Coupled Fourth Order Telegraph Diffusion Framework Using Grayscale Indicators for Image Despeckling
Manish Kumar, Rajendra K. Ray
Speckle noise severely limits the quality of images acquired from coherent imaging systems such as Synthetic Aperture Radar (SAR) and medical ultrasound. Traditional second-order P…
Single Image Defogging Using a Fourth-Order Telegraph PDE Guided by Physical Haze Modeling
Manish Kumar, Rajendra K. Ray
In real-world scenarios, image defogging is an inverse problem due to unknown scene depth, atmospheric scattering, and the common absence of ground truth . To resolve the issue, we…
An Efficient Wavelet-based Physics Informed Residual Neural Networks for Flow Field Reconstruction with Extremely Sparse Data
Biswanath Barman, Rajendra K. Ray
This paper introduces wavelet-physics-informed residual neural networks (W-PIRNNs) to study complex fluid flow problems by reconstructing the flow field from highly sparse, supervi…
A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
Biswanath Barman, Debdeep Chatterjee, Rajendra K. Ray
Traditional computational fluid dynamics and physics-informed neural networks (PINNs) often suffer from high computational cost, mesh sensitivity, and reduced accuracy for strongly…
New Fourth-Order Grayscale Indicator-Based Telegraph Diffusion Model for Image Despeckling
Rajendra K. Ray, Manish Kumar
Second-order PDE models have been widely used for suppressing multiplicative noise, but they often introduce blocky artifacts in the early stages of denoising. To resolve this, we…