4 citations · 7 across the 6 of their papers we have counts for
6 papers
A High-Order Arbitrary Lagrangian-Eulerian Discontinuous Galerkin Method for the Boltzmann Equation in Nearly Incompressible Flows
Atakan Aygun, Onur Ata, Tim Warburton +1
We propose the arbitrary Lagrangian-Eulerian (ALE) form of the Galerkin-Boltzmann formulation for the simulation of nearly incompressible flows with moving boundaries. The continuo…
Hybrid ROM-PINN Framework for Closure Modeling in Convection-Dominated Systems
Ferhat Kaya, Birgul Koc, Atakan Aygun +2
Reduced-order models (ROMs) have become an essential tool for reducing the computational cost of fluid flow simulations. While standard ROMs can efficiently approximate laminar flo…
Implicit Large Eddy Simulation of Nearly Incompressible Flows with a Discontinuous Galerkin-Boltzmann Formulation
Onur Ata, Atakan Aygun, Tim Warburton +1
We present a high-order implicit large eddy simulation (ILES) approach for simulating flows at the nearly incompressible regime. Our methodology based on utilization of a nodal dis…
Physics-Informed Neural Networks for Weakly Compressible Flows Using Galerkin-Boltzmann Formulation
Atakan Aygun, Ali Karakus
In this work, we study the Galerkin-Boltzmann formulation within a physics-informed neural network (PINN) framework to solve flow problems in weakly compressible regimes. The Galer…
Physics-Informed Neural Networks for Mesh Deformation with Exact Boundary Enforcement
Atakan Aygun, Romit Maulik, Ali Karakus
In this work, we have applied physics-informed neural networks (PINN) for solving mesh deformation problems. We used the collocation PINN method to capture the new positions of the…
Physics Informed Neural Networks for Two Dimensional Incompressible Thermal Convection Problems
Atakan Aygun, Ali Karakus
Physics informed neural networks (PINNs) have drawn attention in recent years in engineering problems due to their effectiveness and ability to tackle the problems without generati…