2 citations · 3 across the 3 of their papers we have counts for
3 papers
math.NA2024
Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems
Thivin Anandh, Divij Ghose, Himanshu Jain +3
This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convect…
physics.flu-dyn2024★ 1 cited
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…
cs.LG2024★ 2 cited
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…