Skip to content

Author

T.Vengatesh

We have 3 of 3 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

PARTIAL DIFFERENTIAL EQUATION (PDE) SOLVERS ENHANCED BY DEEP LEARNING FOR GENOMIC FLUID DYNAMICS

A hybrid architecture combining Physics-Informed Neural Networks (PINNs) with operator learning techniques to efficiently solve nonlinear PDEs governing blood flow in viscoelastic arteries, with emphasis on the influence of external magnetic fields and genomic-scale parameter variations is proposed.

C. Manigandan, S. Jeyarani, S.Selvakumar et al. · 0 citations
Open access Jul 2026

CONDITION NUMBER-AWARE PRUNING: PRESERVING MATHEMATICAL STABILITY IN SPARSE NEURAL NETWORKS

This paper proposes a condition number-aware pruning framework that explicitly preserves mathematical stability during the pruning process, and significantly improves both standard accuracy and adversarial robustness compared to conventional pruning methods.

Jeromy R, K Bhavani, K.Mohana Lakshmi et al. · 0 citations
Open access Jul 2026

DISTRIBUTED INTELLIGENCE AT THE EDGE: A MATHEMATICAL FRAMEWORK FOR DECENTRALIZED LEARNING IN IOT NETWORKS

A novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality is proposed, offering a scalable, privacy-preserving solution for deploying artificial intelligenc...

N.Durga, A. Mary Posonia, Selvakumar et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.