4 papers
Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
Arun Govind Neelan, Ferdin Sagai Don Bosco, Naveen Sagar Jarugumalli +1
In the realm of computational fluid dynamics, traditional numerical methods, which heavily rely on discretization, typically necessitate the formulation of partial differential equ…
Revisiting Conservativeness in Fluid Dynamics: Failure of Non-Conservative PINNs and a Path-Integral Remedy
Arun Govind Neelan, Ferdin Sagai Don Bosco, Naveen Sagar Jarugumalli +1
The choice between conservative and non-conservative formulations is a fundamental dilemma in CFD. While non-conservative forms offer intuitive modeling in primitive variables, the…
Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU
Aman Mittal, Kasturi Venkata Sai Srikanth, Ferdin Sagai Don Bosco +3
Quantum inspired evolutionary optimization leverages quantum computing principles like superposition, interference, and probabilistic representation to enhance classical evolutiona…
Benchmarking of GPU-optimized Quantum-Inspired Evolutionary Optimization Algorithm using Functional Analysis
Kandula Eswara Sai Kumar, Supreeth B S, Rajas Dalvi +5
This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classic…