2 citations · 4 across the 4 of their papers we have counts for
4 papers
An efficient hp-Variational PINNs framework for incompressible Navier-Stokes equations
Thivin Anandh, Divij Ghose, Ankit Tyagi +3
Physics-informed neural networks (PINNs) are able to solve partial differential equations (PDEs) by incorporating the residuals of the PDEs into their loss functions. Variational P…
FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries
Thivin Anandh, Divij Ghose, Himanshu Jain +1
Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Tra…
An Operator-Splitting Finite Element Method for the Numerical Solution of Radiative Transfer Equation
Sashikumaar Ganesan, Maneesh Kumar Singh
An operator-splitting finite element scheme for the time-dependent, high-dimensional radiative transfer equation is presented in this paper. The streamline upwind Petrov-Galerkin f…
An object oriented parallel finite element scheme for computations of PDEs: Design and implementation
Sashikumaar Ganesan, Volker John, Gunar Matthies +3
Parallel finite element algorithms based on object-oriented concepts are presented. Moreover, the design and implementation of a data structure proposed are utilized in realizing a…