arXiv subjects

Ram Jiwari

Publications and source records attributed to Ram Jiwari.

4 recordsLinked to original sources

arXiv2025

PINNs Algorithmic Framework for Simulation of Nonlinear Burgers' Type Models

In this work, a physics-informed neural networks (PINNs) based algorithm is used for simulation of nonlinear 1D and 2D Burgers' type models. This scheme relies on a neural network built to approximate the problem solution and use a trial function that meets the initial data and boundary criteria. First of all, a brief mathematical formulation of the problem and the structure of PINNs, including the neural network architecture, loss construction, and training methodology is described. Finally, the algorithm is demonstrated with five test problems involving variations of the 1D coupled, 2D single and 2D coupled Burgers' models. We compare the PINN-based solutions with exact results to assess accuracy and convergence of the developed algorithm. The results demonstrate that PINNs may faithfully replicate nonlinear PDE solutions and offer competitive performance in terms of inaccuracy and flexibility. This work demonstrates the potential of PINNs as a reliable approach to solving complex time-dependent PDEs.

cs.LG↗

arXiv2025

rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics

The variability in ECG readings influenced by individual patient characteristics has posed a considerable challenge to adopting automated ECG analysis in clinical settings. A novel feature fusion technique termed SACC (Self Attentive Canonical Correlation) was proposed to address this. This technique is combined with DPN (Dual Pathway Network) and depth-wise separable convolution to create a robust, interpretable, and fast end-to-end arrhythmia classification model named rECGnition_v2.0 (robust ECG abnormality detection). This study uses MIT-BIH, INCARTDB and EDB dataset to evaluate the efficiency of rECGnition_v2.0 for various classes of arrhythmias. To investigate the influence of constituting model components, various ablation studies were performed, i.e. simple concatenation, CCA and proposed SACC were compared, while the importance of global and local ECG features were tested using DPN rECGnition_v2.0 model and vice versa. It was also benchmarked with state-of-the-art CNN models for overall accuracy vs model parameters, FLOPs, memory requirements, and prediction time. Furthermore, the inner working of the model was interpreted by comparing the activation locations in ECG before and after the SACC layer. rECGnition_v2.0 showed a remarkable accuracy of 98.07% and an F1-score of 98.05% for classifying ten distinct classes of arrhythmia with just 82.7M FLOPs per sample, thereby going beyond the performance metrics of current state-of-the-art (SOTA) models by utilizing MIT-BIH Arrhythmia dataset. Similarly, on INCARTDB and EDB datasets, excellent F1-scores of 98.01% and 96.21% respectively was achieved for AAMI classification. The compact architectural footprint of the rECGnition_v2.0, characterized by its lesser trainable parameters and diminished computational demands, unfurled several advantages including interpretability and scalability.

eess.SP↗

arXiv2024

Finite Element Simulation of Modified Poisson-Nernst-Planck/Navier-Stokes Model for Compressible Electrolytes under Mechanical Equilibrium

This work presents a finite element method for a modified Poisson-Nernst-Planck/Navier-Stokes (PNP/NS) model under the mechanical equilibrium, developed for compressible electrolytes. Another key contribution of this work is the reduction of the equilibrium system to a modified Poisson-Boltzmann system. The proposed numerical scheme is capable of handling both compressible and incompressible regimes by employing a bulk modulus parameter, which governs the fluid's compressibility and enables seamless transition between these regimes. To emphasize practical relevance, we discuss the implications of compressible electrolytes in the context of double-layer capacitance behavior. We also conduct numerical simulations over various domains to demonstrate its applicability under various operating conditions, including temperature fluctuations and variations in the bulk modulus. The numerical results validate the accuracy and robustness of our computational scheme and demonstrate that the observed limiting behavior for the incompressible regime aligns with the theoretical trends anticipated by Dreyer et al. [39].

math.NA↗

arXiv2024

Conformal Finite Element Methods for Nonlinear Rosenau-Burgers-Biharmonic Models

We present a novel and comparative analysis of finite element discretizations for a nonlinear Rosenau-Burgers model including a biharmonic term. We analyze both continuous and mixed finite element approaches, providing stability, existence, and uniqueness statements of the corresponding variational methods. We also obtain optimal error estimates of the semidiscrete scheme in corresponding Bôchner spaces. Finally, we construct a fully discrete scheme through a backward Euler discretization of the time derivative, and prove well-posedness statements for this fully discrete scheme. Our findings show that the mixed approach removes some theoretical impediments to analysis and is numerically easier to implement. We provide numerical simulations for the mixed formulation approach using $C^0$ Taylor-Hood finite elements on several domains. Our numerical results confirm that the algorithm has optimal convergence in accordance with the observed theoretical results.

math.NA↗